Project OverviewΒΆ

Computer vision can be used to automate supervision and trigger appropriate actions if an event is predicted from an image of interest. For example, food items can be easily identified by a camera based on:

  • The type of food

  • Color

  • Ingredients, etc.

πŸ“Š Data DescriptionΒΆ

The Food101 dataset contains 16,256 images across 17 food classes.

Each class can be roughly split into 70% training and 30% testing.

Classes are typically food names (e.g., apple_pie, chicken_curry).

The dataset has already been attached with this project. Please use the same version provided.

πŸ“Ž Original Dataset Link for Reference Only:ΒΆ

Food101 Dataset on Kaggle

πŸ“– ReferenceΒΆ

The dataset was originally introduced in the paper: "Food-101 – Mining Discriminative Components with Random Forests" Authors: Lukas Bossard, Matthieu Guillaumin, Luc Van Gool

Import all libiraryΒΆ

InΒ [Β ]:
import os
import numpy as np                                                                               # Importing numpy for Matrix Operations
import pandas as pd                                                                              # Importing pandas to read CSV files
import matplotlib.pyplot as plt                                                                  # Importting matplotlib for Plotting and visualizing images
import math                                                                                      # Importing math module to perform mathematical operations
import cv2                                                                                       # Importing openCV for image processing
import seaborn as sns                                                                            # Importing seaborn to plot graphs
from collections import defaultdict
import random
import shutil
import os

# Tensorflow modules
import tensorflow as tf
from sklearn.model_selection import train_test_split
from tensorflow.keras.preprocessing.image import ImageDataGenerator
from tensorflow.keras.models import Sequential # Importing the sequential module to define a sequential model
from keras.callbacks import ReduceLROnPlateau, ModelCheckpoint, EarlyStopping
from keras.layers import Dense, Dropout, Flatten, Conv2D, MaxPooling2D,MaxPool2D, GlobalAveragePooling2D, Input, BatchNormalization, Multiply, Activation
from tensorflow.keras.optimizers import Adam,SGD,Optimizer
from tensorflow.keras.utils import to_categorical


# sklearn lib
from sklearn.metrics import accuracy_score
from sklearn.metrics import confusion_matrix
from keras import backend as K
from sklearn.model_selection import train_test_split # train test split
from sklearn.preprocessing import LabelEncoder # label encoder
from sklearn.metrics import accuracy_score, classification_report

# Set seeds for reproducibility
seed_value = 42

random.seed(seed_value)
np.random.seed(seed_value)
tf.random.set_seed(seed_value)
os.environ['PYTHONHASHSEED'] = str(seed_value)
InΒ [Β ]:
# Uncomment and run the following code in case Google Colab is being used
from google.colab import drive
drive.mount('/content/drive')

Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount("/content/drive", force_remount=True).
InΒ [Β ]:
!unzip '/content/drive/MyDrive/Python Course_shared/computer Vision/Food_101.zip'

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InΒ [Β ]:
# Image folder
food_101_image_folder = '/content/Food_101/'

Overview of Original Dataset Images by Class (No Bounding Boxes)ΒΆ

Visual Inspection of Annotated Images (With Bounding Boxes)ΒΆ

The Food101 dataset contains a total of 16,256 images categorized into 17 different food classes. Each class represents a type of food, such as apple_pie, pizza, samosa, etc.

This section presents a visual inspection of sample images with bounding box annotations, which highlight the region of interest (i.e., the food item) within each image.

Key Details:ΒΆ

  • The dataset can be split into training and testing sets in a 70-30 ratio per class.
  • Each food category has approximately 1,000 images, except for apple_pie, which has fewer samples.
  • Bounding boxes help localize the food item in the image, which is useful for object detection or improving classification accuracy by focusing the model on relevant image regions.

Purpose of this Visualization:ΒΆ

  • To visually verify the quality and accuracy of bounding box annotations.
  • To understand how localized regions differ across classes and image samples.
  • To evaluate whether bounding box information can aid in better feature learning for classification or detection tasks.

Below, we inspect a few representative examples from different classes with their bounding boxes overlaid.

Helper FunctionsΒΆ

InΒ [Β ]:
# Metadat Class for storing it
class IdentityMetadata():
    def __init__(self, base, name, file, size):
        # print(base, name, file)
        # dataset base directory
        self.base = base
        # identity name
        self.name = name
        # image file name
        self.file = file
        self.size=size

    def __repr__(self):
        return self.image_path()

    def image_path(self):
        return os.path.join(self.base, self.name, self.file)

def load_metadata(path):
    """
    Load metadata for all images in a directory
    """
    if not os.path.exists(path):
        raise ValueError(f"Path does not exist: {path}")

    metadata = []
    for index, folder in enumerate(os.listdir(path)):
        subdir_path = os.path.join(path, folder)
        if not os.path.isdir(subdir_path):
            continue  # Skip files like .DS_Store or any non-folder

        # Get all valid image files in the subdirectory
        image_files = [f for f in os.listdir(subdir_path)
                       if f.lower().endswith(('.jpg', '.jpeg'))]

        total_images = len(image_files)

        for file in image_files:
            metadata.append(IdentityMetadata(path, folder, file, total_images))

    return np.array(metadata)

# Read & Load Image
def load_image(path):
  """
  Load image from path
  """
  img = cv2.imread(path)
  # Convert to RGB
  return cv2.cvtColor(img, cv2.COLOR_BGR2RGB)

def split_metadata_per_class(metadata_list, train_ratio=0.7, seed=42):
    random.seed(seed)

    # Step 1: Group by identity (class name)
    class_groups = defaultdict(list)
    for meta in metadata_list:
        class_groups[meta.name].append(meta)

    train_set = []
    test_set = []

    # Step 2: Shuffle and split each class group
    for identity, items in class_groups.items():
        random.shuffle(items)
        split_index = int(len(items) * train_ratio)
        train_set.extend(items[:split_index])
        test_set.extend(items[split_index:])

    return train_set, test_set


def organize_dataset(metadata_list, base_output_dir):
    """
    Organize dataset into subdirectories by class
    """
    if not os.path.exists(base_output_dir):
        os.makedirs(base_output_dir)

    for meta in metadata_list:
        label_dir = os.path.join(base_output_dir, meta.name)
        os.makedirs(label_dir, exist_ok=True)
        src = meta.image_path()
        dst = os.path.join(label_dir, meta.file)
        shutil.copy2(src, dst)


def draw_yolo_bboxes_from_csv(data_dir, image_file, annotations_df, ax=None, food_class='images'):
    images_dir = os.path.join(data_dir, food_class)
    image_path = os.path.join(images_dir, image_file)

    # Load image
    image = cv2.imread(image_path)
    if image is None:
        print(f"❌ Image not found: {image_path}")
        return
    image_rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
    height, width, _ = image.shape

    # Filter annotations for this image
    boxes = annotations_df[annotations_df['image'] == image_file]

    if boxes.empty:
        print(f"⚠️ No annotations found for: {image_file}")
        return

    # Draw each bounding box
    for _, row in boxes.iterrows():
        class_id = row['class_name']
        x_center = float(row['x_center']) * width
        y_center = float(row['y_center']) * height
        w = float(row['width']) * width
        h = float(row['height']) * height

        x1 = int(x_center - w / 2)
        y1 = int(y_center - h / 2)
        x2 = int(x_center + w / 2)
        y2 = int(y_center + h / 2)

        # Draw rectangle and class_id
        # Draw rectangle and class_id
        cv2.rectangle(image_rgb, (x1, y1), (x2, y2), (0, 0, 255), 2)  # Changed to blue (BGR format)
        cv2.putText(image_rgb, str(class_id), (x1, y1 - 10),
                    cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 255), 2)  # Changed to blue (BGR format)

    if ax is None:
        plt.figure(figsize=(40, 40))
        plt.imshow(image_rgb)
        plt.axis('off')
        plt.title(f"{image_file}")
        plt.show()
    else:
        ax.imshow(image_rgb)
        ax.axis('off')
InΒ [Β ]:
# load image metadata from folder
metadata = load_metadata(food_101_image_folder)

# Print metadata shape to get total number of images
print("Total number of images:", metadata.shape)

# Print folder path
print("\nDataset folder path:", food_101_image_folder)

# Get unique food class names from metadata
food_classes = sorted(list(set(item.name for item in metadata)))

# Print food class names
print("\nFood classes in dataset:")
for food_class in food_classes:
    print(f"- {food_class}")

Total number of images: (16256,)

Dataset folder path: /content/Food_101/

Food classes in dataset:
- apple_pie
- chocolate_cake
- donuts
- falafel
- french_fries
- hot_dog
- ice_cream
- nachos
- onion_rings
- pancakes
- pizza
- ravioli
- samosa
- spring_rolls
- strawberry_shortcake
- tacos
- waffles

Observation:ΒΆ

Total number of imagesΒΆ

  • The dataset consists of 16,256 images.

Dataset folder pathΒΆ

  • The images are located in the folder path: asset/Food_101.

  1. Dataset Size: The dataset contains 16,256 images, which is a reasonable size for training machine learning models. However, you may need to apply data augmentation techniques or preprocessing to account for class imbalances or variations in image content.

  2. Food Class Distribution: It's crucial to analyze the class distribution. Some food classes might have more images than others. To address this, you might want to consider techniques like oversampling, undersampling, or using class weights in the machine learning model.

  3. Class Variety: The dataset includes a wide range of food items, from desserts (apple pie, chocolate cake, ice cream) to savory items (pizza, tacos, falafel). This makes the classification task more challenging due to the variability in shapes, textures, and visual features across food items.

  4. Data Preprocessing: The typical preprocessing steps for image classification include:

    • Resizing images to a consistent size.
    • Normalization (scaling pixel values to a range of 0-1).
    • Data augmentation (rotating, flipping, and adjusting color jitter). These steps help ensure that the model generalizes well and avoids overfitting.

  5. Performance Metrics: Metrics like accuracy, precision, recall, and F1-score should be evaluated. These metrics help assess the model's ability to distinguish between different food classes, especially when some are more challenging to classify.

InΒ [Β ]:
# Updated configuration
n_classes_to_sample = 10
samples_per_class = 3
max_images = n_classes_to_sample * samples_per_class

plt.figure(figsize=(18, 12))
seen = {}
plotted = 0

for item in metadata:
    class_name = item.name
    if seen.get(class_name, 0) < samples_per_class:
        img = cv2.imread(item.image_path())
        if img is None:
            print(f"Warning: Could not read image {item.image_path()}")
            continue

        img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
        height, width, channels = img.shape
        is_grayscale = channels == 1

        plt.subplot(n_classes_to_sample, samples_per_class, plotted + 1)
        plt.imshow(img)
        plt.title(f"{class_name}\n{width}x{height}\n{'Gray' if is_grayscale else 'RGB'}")
        plt.axis('off')

        seen[class_name] = seen.get(class_name, 0) + 1
        plotted += 1

        if plotted >= max_images:
            break

plt.tight_layout()
plt.show()
No description has been provided for this image

Observations:ΒΆ

  • Consistent Color Space:

    • All images are RGB, which is standard and expected for deep learning models using convolutional networks.

  • Variable Image Dimensions:

    • Although most images are square (512x512), several have non-square resolutions (e.g., 512x342, 382x512, 512x384).
    • This introduces aspect ratio variability, which may affect model performance if not handled properly.

  • Centered Composition:

    • Most food items appear well-centered, with minimal background clutter.
    • This suggests that the bounding boxes or crops (if applied) were done with reasonable accuracy.

  • Intra-class Variation:

    • Each class (e.g., samosa, falafel, apple_pie) shows visual variation in texture, orientation, and plating.
    • This is beneficial for training robust classifiers but may require data augmentation to generalize well.

  • Preprocessing Needed:

    • Normalize image sizes (e.g., resize all to 224Γ—224 or 299Γ—299 depending on model input).
    • Consider aspect ratio preservation or intelligent cropping.

  • Data Augmentation Recommended:

    • Techniques like random cropping, rotation, zoom, and brightness adjustments will improve generalization.

  • Improves Model Generalization:

    • Bounding boxes help the model learn discriminative visual patterns with greater consistency across the dataset.
InΒ [Β ]:
# Create a DataFrame to show class distribution
class_counts = {}

for class_name in os.listdir(food_101_image_folder):
    if os.path.isdir(os.path.join(food_101_image_folder, class_name)):
        class_size = len(os.listdir(os.path.join(food_101_image_folder, class_name)))
        class_counts[class_name] = class_size

df = pd.DataFrame(list(class_counts.items()), columns=['class_name', 'class_size'])
df.set_index('class_name', inplace=True)
df
Out[Β ]:
class_size
class_name
tacos 1000
falafel 1000
donuts 1000
samosa 1000
french_fries 1000
pancakes 1000
hot_dog 1000
strawberry_shortcake 1000
onion_rings 1000
ravioli 1000
nachos 1000
apple_pie 257
ice_cream 1000
waffles 1000
chocolate_cake 1000
pizza 1000
spring_rolls 1000
InΒ [Β ]:
# show in barplot
sns.barplot(x=df.index, y=df['class_size'],color='r')
plt.xticks(rotation=90)
plt.show()
No description has been provided for this image

Observation:ΒΆ

  1. Uniform Distribution Across Most Classes

    • Out of the 17 total food classes, 16 classes have exactly 1000 images.
    • This indicates a mostly well-balanced dataset, which is beneficial for training classification models without inherent bias.

  2. Class Imbalance – apple_pie

    • The apple_pie class has only 257 images, which is significantly lower than the others.
    • This imbalance may negatively affect the model's ability to correctly classify apple_pie, leading to:
      • Lower recall and precision for the minority class.
      • Potential model bias toward majority classes.

  3. Potential Mitigation Strategies
    To address the class imbalance:

    • Data Augmentation: Apply augmentation (rotation, flipping, color jittering) to synthetically increase the number of apple_pie images.
    • Class Weights: Use class weights in the loss function during training to emphasize learning on the minority class.
    • SMOTE or Oversampling Techniques (if suitable for images).
    • Undersampling other classes (less preferred due to loss of valuable data).

  4. Impact on Model Evaluation

    • Use evaluation metrics such as F1-score, Recall, and Confusion Matrix in addition to Accuracy.
    • These metrics will better highlight the model’s performance on the imbalanced class.

  5. Need for Visual Exploration

    • Visual inspection of apple_pie images may help understand why the count is low (e.g., ambiguous visuals, data collection issue).

Visual Inspection of Annotated Images (With Bounding Boxes)ΒΆ

The Food101 dataset contains a total of 16,256 images categorized into 17 different food classes. Each class represents a type of food, such as apple_pie, pizza, samosa, etc.

This section presents a visual inspection of sample images with bounding box annotations, which highlight the region of interest (i.e., the food item) within each image.

Key Details:ΒΆ

  • The dataset can be split into training and testing sets in a 70-30 ratio per class.
  • Each food category has approximately 1,000 images, except for apple_pie, which has fewer samples.
  • Bounding boxes help localize the food item in the image, which is useful for object detection or improving classification accuracy by focusing the model on relevant image regions.

Purpose of this Visualization:ΒΆ

  • To visually verify the quality and accuracy of bounding box annotations.
  • To understand how localized regions differ across classes and image samples.
  • To evaluate whether bounding box information can aid in better feature learning for classification or detection tasks.

Below, we inspect a few representative examples from different classes with their bounding boxes overlaid.

Dataset Annotation OverviewΒΆ

We have successfully annotated our image dataset and stored the results in a file named annotation_csv. This file adheres to the YOLO format, with the following columns:

  • image_name: Name of the image file.
  • class_name: Object category label.
  • x_center, y_center, width, height: Bounding box coordinates normalized to the [0, 1] range.

How We Annotated the DataΒΆ

We used a hybrid approach combining automated tools and AI assistance to efficiently generate high-quality annotations:

  • YOLOv8 and YOLOv11: Employed for automatic object detection and initial bounding box generation.

  • ChatGPT API: Used to pass images directly and receive bounding box predictions for objects, especially helpful in cases requiring semantic understanding or where pretrained models struggled.

  • Roboflow: Used as a platform for annotation review, correction, and dataset management. It helped streamline the export process into YOLO format and ensured consistency.

Usage in Training PipelineΒΆ

The annotation_csv will be used for:

  • Data Preparation: Parsing annotations in YOLO format for image-label mapping.
  • Model Training: Feeding normalized bounding boxes and class labels into the training pipeline.

This multi-tool approach ensures a robust, scalable, and AI-assisted annotation pipeline tailored for object detection tasks.

Loading Annotation Data and Extracting Food ClassesΒΆ

In this step, we load the refined annotation data from the Food_101_Annotation directory. The annotation file (annotation_refined.csv) contains important metadata such as image file names, bounding box coordinates, and corresponding food class labels.

We extract and display the list of unique food classes present in the dataset to understand the classification scope.

InΒ [Β ]:
!unzip '/content/drive/MyDrive/Python Course_shared/computer Vision/annotation/Food-101-Annotated-V3.zip'

Archive:  /content/drive/MyDrive/Python Course_shared/computer Vision/annotation/Food-101-Annotated-V3.zip
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InΒ [Β ]:
# Correct data directory path
data_dir = 'Food-101-Annotated/'

# Read the annotation file
annotations_df = pd.read_csv('Food-101-Annotated/annotation_refined.csv')

classes = annotations_df['class_name'].unique()

print("Available food classes:")
classes.tolist()

Available food classes:
Out[Β ]:
['french_fries',
 'nachos',
 'chocolate_cake',
 'samosa',
 'ice_cream',
 'strawberry_shortcake',
 'pizza',
 'tacos',
 'waffle',
 'onion_ring']
InΒ [Β ]:
# Display first few rows of annotations dataframe
print("\nSample of Annotations Data:")
annotations_df.head()

Sample of Annotations Data:
Out[Β ]:
image class_name x_center y_center width height
0 1199754.jpg french_fries 0.509375 0.522656 0.720313 0.771875
1 1232631.jpg nachos 0.178125 0.717969 0.351562 0.439063
2 1232631.jpg nachos 0.522656 0.511719 0.932813 0.954688
3 2616112.jpg nachos 0.165625 0.526563 0.214844 0.465625
4 2616112.jpg nachos 0.394531 0.482812 0.171875 0.379688
InΒ [Β ]:
# Display summary statistics of annotations
print("\nAnnotations DataFrame Info:")
annotations_df.info()

Annotations DataFrame Info:
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 1090 entries, 0 to 1089
Data columns (total 6 columns):
 #   Column      Non-Null Count  Dtype  
---  ------      --------------  -----  
 0   image       1090 non-null   object 
 1   class_name  1090 non-null   object 
 2   x_center    1090 non-null   float64
 3   y_center    1090 non-null   float64
 4   width       1090 non-null   float64
 5   height      1090 non-null   float64
dtypes: float64(4), object(2)
memory usage: 51.2+ KB
InΒ [Β ]:
# Display value counts of classes
print("\nClass Distribution:")
annotations_df['class_name'].value_counts()

Class Distribution:
Out[Β ]:
count
class_name
samosa 193
onion_ring 176
nachos 143
ice_cream 125
tacos 117
french_fries 81
pizza 68
strawberry_shortcake 67
waffle 65
chocolate_cake 55

InΒ [Β ]:
# Display value counts of classes
print("\nClass Distribution:")
annotations_df.describe()

Class Distribution:
Out[Β ]:
x_center y_center width height
count 1090.000000 1090.000000 1090.000000 1090.000000
mean 0.503608 0.485872 0.533300 0.502740
std 0.174767 0.152479 0.257276 0.234620
min 0.067187 0.102344 0.001563 0.002344
25% 0.397656 0.392383 0.327539 0.314844
50% 0.503125 0.492188 0.487109 0.463672
75% 0.603125 0.572656 0.753906 0.683594
max 0.953125 0.920312 1.000000 1.000000

Visualizing Sample Images with Bounding Boxes for Each Food ClassΒΆ

This section displays a visual grid of sample images from the Food-101 dataset, annotated with bounding boxes for each food class. The goal is to provide a clear, visual understanding of how food items are localized within images and to verify the accuracy of the annotations.

InΒ [Β ]:
for i, class_name in enumerate(classes, 1):
    print(f"{i}. {class_name}")

# Process each class separately with 6 images in a 2x3 grid
for food_class in classes:
    print(f"\nProcessing class: {food_class}")

    # Get all unique images for this class
    class_images = annotations_df[annotations_df['class_name'] == food_class]['image'].unique()

    # Select 6 random images
    selected_images = random.sample(list(class_images), min(6, len(class_images)))

    # Create subplot grid for this class
    fig, axes = plt.subplots(2, 3, figsize=(15, 10))
    axes = axes.ravel()

    # Display each image
    for img_idx, image_file in enumerate(selected_images):
        # Draw bounding boxes on the subplot
        draw_yolo_bboxes_from_csv(data_dir, image_file, annotations_df, axes[img_idx], food_class)

    plt.suptitle(f"{food_class} Samples with Bounding Boxes", y=1.02, fontsize=14)
    plt.tight_layout()
    plt.show()

# Display dataset statistics
print("\nDataset Statistics:")
print(f"Total number of annotations: {len(annotations_df)}")
print(f"Total number of unique images: {len(annotations_df['image'].unique())}")
print(f"Total number of classes: {len(classes)}")

1. french_fries
2. nachos
3. chocolate_cake
4. samosa
5. ice_cream
6. strawberry_shortcake
7. pizza
8. tacos
9. waffle
10. onion_ring

Processing class: french_fries
No description has been provided for this image 
Processing class: nachos
No description has been provided for this image 
Processing class: chocolate_cake
No description has been provided for this image 
Processing class: samosa
No description has been provided for this image 
Processing class: ice_cream
No description has been provided for this image 
Processing class: strawberry_shortcake
No description has been provided for this image 
Processing class: pizza
No description has been provided for this image 
Processing class: tacos
No description has been provided for this image 
Processing class: waffle
No description has been provided for this image 
Processing class: onion_ring
No description has been provided for this image 
Dataset Statistics:
Total number of annotations: 1090
Total number of unique images: 559
Total number of classes: 10

Model BuildingΒΆ

Performance Metrics: Metrics like accuracy, precision, recall, and F1-score should be evaluated. These metrics help assess the model's ability to distinguish between different food classes, especially when some are more challenging to classify.

Preparing Data Set (Annotated) For ModelΒΆ

We have annotated the datset and created a seperate annotation_csv file which contains imagename,classname and xcenter,ycenter,width,height (yolo format) all normalised 0to1.We will be using the same dataset for the model data preprartion and training

InΒ [Β ]:
# base path of annotated images
base_path='Food-101-Annotated/'
InΒ [Β ]:
# load image metadata from folder for training and testing
metadata_annotated = load_metadata(base_path)
annotation_df_model=pd.read_csv(f"{base_path}/annotation_refined.csv")

Train Test Split With Image GeneratorΒΆ

InΒ [Β ]:
# Take first image only as there might have duplicate entryfor same image as same image mutiple instances of same food item
classification_df = annotation_df_model.groupby('image').first().reset_index()
# create a new volumn file name by concating the classname this will help us in using df for image data generator
classification_df['image_name'] = classification_df.apply(lambda row: f"{row['class_name']}/{row['image']}", axis=1)
# train test split with dividing the class in same propertion
train_df, temp_df = train_test_split(
    classification_df,
    test_size=0.2,
    stratify=classification_df['class_name'],  # ensures class distribution is preserved
    random_state=42)
valid_df, test_df = train_test_split(
    temp_df,
    test_size=0.5,
    stratify=temp_df['class_name'],  # ensures class distribution is preserved
    random_state=42)
InΒ [Β ]:
print(f"Training Set ->{train_df.shape}",f"Validation Set ->{valid_df.shape}",f"Test Set ->{test_df.shape}")

Training Set ->(447, 7) Validation Set ->(56, 7) Test Set ->(56, 7)
InΒ [Β ]:
df_1=classification_df.groupby('class_name').agg({'image':'count'}).reset_index()
df_2=train_df.groupby('class_name').agg({'image':'count'}).reset_index()
# Rename columns for clarity
df_1 = df_1.rename(columns={'image': 'total_count'})
df_2 = df_2.rename(columns={'image': 'train_count'})

# Merge on 'class_name'
result_df = pd.merge(df_1, df_2, on='class_name', how='outer')
result_df
Out[Β ]:
class_name total_count train_count
0 chocolate_cake 51 41
1 french_fries 78 62
2 ice_cream 54 43
3 nachos 48 38
4 onion_ring 50 40
5 pizza 58 46
6 samosa 71 57
7 strawberry_shortcake 50 40
8 tacos 52 42
9 waffle 47 38
  • We can see we have 447 for training 56 for testing and 56 will be for valiadation
  • We can see each class is equally distributed 80% of total image set for the class from the aboce dataframe
InΒ [Β ]:
# Define Image data generator for train and testvalidation
train_datagen =ImageDataGenerator(
                 horizontal_flip=True,
                 vertical_flip=True,
                 rotation_range=15,
                 width_shift_range=0.1,
                 height_shift_range=0.1,
                 zoom_range=0.2,
                 shear_range=0.1,
                 fill_mode='nearest',
                 rescale=1/255) #rescale to [0-1], add zoom range of 0.2x and horizontal flip
test_valid_datagen = ImageDataGenerator(rescale=1./255)


# Create training image gen
train_gen = train_datagen.flow_from_dataframe(
    train_df,
    directory=base_path,  # base path
    x_col='image_name',
    y_col='class_name',
    target_size=(128, 128),
    class_mode='categorical',
    batch_size=32,
    shuffle=True
)

# Create test image gen
test_gen = test_valid_datagen.flow_from_dataframe(
    test_df,
    directory=base_path,
    x_col='image_name',
    y_col='class_name',
    target_size=(128, 128),
    class_mode='categorical',
    batch_size=32,
    shuffle=False
)
# Create valid image gen

valid_gen = test_valid_datagen.flow_from_dataframe(
    valid_df,
    directory=base_path,
    x_col='image_name',
    y_col='class_name',
    target_size=(128, 128),
    class_mode='categorical',
    batch_size=32,
    shuffle=False
)

Found 446 validated image filenames belonging to 10 classes.
Found 56 validated image filenames belonging to 10 classes.
Found 56 validated image filenames belonging to 10 classes.

/usr/local/lib/python3.11/dist-packages/keras/src/legacy/preprocessing/image.py:920: UserWarning: Found 1 invalid image filename(s) in x_col="image_name". These filename(s) will be ignored.
  warnings.warn(
InΒ [Β ]:
print(train_gen.class_indices)
print(valid_gen.class_indices)
print(test_gen.class_indices)

{'chocolate_cake': 0, 'french_fries': 1, 'ice_cream': 2, 'nachos': 3, 'onion_ring': 4, 'pizza': 5, 'samosa': 6, 'strawberry_shortcake': 7, 'tacos': 8, 'waffle': 9}
{'chocolate_cake': 0, 'french_fries': 1, 'ice_cream': 2, 'nachos': 3, 'onion_ring': 4, 'pizza': 5, 'samosa': 6, 'strawberry_shortcake': 7, 'tacos': 8, 'waffle': 9}
{'chocolate_cake': 0, 'french_fries': 1, 'ice_cream': 2, 'nachos': 3, 'onion_ring': 4, 'pizza': 5, 'samosa': 6, 'strawberry_shortcake': 7, 'tacos': 8, 'waffle': 9}
InΒ [Β ]:
from collections import Counter

# Count how many instances per class
class_counts = Counter(test_gen.classes)

# Map class indices back to names
index_to_class = {v: k for k, v in test_gen.class_indices.items()}
class_distribution = {index_to_class[i]: count for i, count in class_counts.items()}

# Print result
for class_name, count in class_distribution.items():
    print(f"{class_name}: {count}")

ice_cream: 6
french_fries: 8
chocolate_cake: 5
onion_ring: 5
strawberry_shortcake: 5
samosa: 7
tacos: 5
nachos: 5
pizza: 6
waffle: 4

Observations:ΒΆ

We can see each classes have some images in test set

Train Test Split Normal (without image generator)ΒΆ

InΒ [Β ]:
images = []
labels = []
for i in metadata_annotated:
  img = cv2.imread(i.image_path())
  if img is None:
    continue
  img_rgb = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
  img_resized = cv2.resize(img_rgb, (128, 128))
  images.append(img_resized/255.0)
  labels.append(i.name)
InΒ [Β ]:
# Convert lists to arrays
X = np.array(images)
y = np.array(labels)

# Encode string labels to integers
label_encoder = LabelEncoder()
y_encoded = label_encoder.fit_transform(y)
y_categorical = to_categorical(y_encoded)  # one-hot encoding

# Train-test split
X_train, X_temp, y_train, y_temp = train_test_split(X, y_categorical, test_size=0.2, random_state=42, stratify=y_categorical)
X_valid, X_test, y_valid, y_test = train_test_split(X_temp, y_temp, test_size=0.5, random_state=42, stratify=y_temp)

# Shapes
print(f"Train: {X_train.shape}, {y_train.shape}")
print(f"Valid: {X_valid.shape}, {y_valid.shape}")
print(f"Test: {X_test.shape}, {y_test.shape}")

Train: (452, 128, 128, 3), (452, 10)
Valid: (56, 128, 128, 3), (56, 10)
Test: (57, 128, 128, 3), (57, 10)

Model CreationΒΆ

Helper FunctionΒΆ

InΒ [Β ]:
def build_basic_cnn(input_shape, num_classes):
    model = Sequential()

    # First Convolutional Block
    model.add(Conv2D(32, (3, 3), activation='relu', input_shape=input_shape))
    model.add(MaxPooling2D((2, 2)))

    # Second Convolutional Block
    model.add(Conv2D(64, (3, 3), activation='relu'))
    model.add(MaxPooling2D((2, 2)))

    # Third Convolutional Block (Optional, but good for slightly more complexity)
    model.add(Conv2D(128, (3, 3), activation='relu'))
    model.add(MaxPooling2D((2, 2)))

    # Flatten the 3D output to 1D for the Dense layers
    model.add(Flatten())

    # Dense (Fully Connected) Layers
    model.add(Dense(128, activation='relu'))
    model.add(Dropout(0.5)) # Dropout for regularization to prevent overfitting
    model.add(Dense(num_classes, activation='softmax')) # Output layer for classification

    # Compile the model
    model.compile(optimizer=Adam(learning_rate=0.001),
                  loss='categorical_crossentropy',
                  metrics=['accuracy'])
    return model
InΒ [Β ]:
def build_basic_cnn_2(input_shape, num_classes, optimizer: Optimizer = Adam(learning_rate=0.001)):
    model = Sequential()

    # First Convolutional Block
    model.add(Conv2D(32, (3, 3), activation='relu', input_shape=input_shape, kernel_initializer='he_normal'))
    model.add(MaxPooling2D((2, 2)))

    # Second Convolutional Block
    model.add(Conv2D(64, (3, 3), activation='relu'))
    model.add(MaxPooling2D((2, 2)))

    # Third Convolutional Block
    model.add(Conv2D(128, (3, 3), activation='relu'))
    model.add(MaxPooling2D((2, 2)))

    # Flatten and Fully Connected Layers
    model.add(Flatten())
    model.add(Dense(128, activation='relu'))
    model.add(Dropout(0.5))
    model.add(Dense(num_classes, activation='softmax'))

    # Compile with the given optimizer
    model.compile(optimizer=optimizer,
                  loss='categorical_crossentropy',
                  metrics=['accuracy'])

    return model
InΒ [Β ]:
def get_callbacks(filepath='cnn_model.weights.h5'):
    """
    Returns a tuple of callbacks: ModelCheckpoint, EarlyStopping, and ReduceLROnPlateau.

    Parameters:
    -----------
    filepath : str
        Path to save the model weights.

    Returns:
    --------
    tuple :
        (ModelCheckpoint, EarlyStopping, ReduceLROnPlateau)
    """
    checkpointer = ModelCheckpoint(
        filepath=filepath,
        verbose=1,
        save_best_only=True,
        save_weights_only=True
    )

    earlystopping = EarlyStopping(
        monitor='val_loss',
        min_delta=0.01,
        patience=20,
        mode='auto'
    )

    reduceLR = ReduceLROnPlateau(
        monitor='val_loss',
        factor=0.5,
        patience=10,
        mode='auto'
    )

    return checkpointer, earlystopping, reduceLR
InΒ [Β ]:
def plot_model_accuracy(history,model):
    """
    Plots the accuracy and loss trends over epochs for a trained neural network.

    Parameters:
    - history: Keras History object containing training metrics.

    Displays:
    - Subplot 1: Training & Validation Accuracy vs. Epochs
    - Subplot 2: Training & Validation Loss vs. Epochs
    """

    h1 = history.history  # Extract training history

    # Get the final values for display in titles
    final_train_acc = h1['accuracy'][-1]
    final_val_acc = h1['val_accuracy'][-1]
    final_train_loss = h1['loss'][-1]
    final_val_loss = h1['val_loss'][-1]

    plt.figure(figsize=(15, 6))

    # Plot Accuracy
    plt.subplot(1, 2, 1)
    sns.lineplot(y=h1['accuracy'], x=range(len(h1['accuracy'])), label="Training Accuracy", color="green")
    sns.lineplot(y=h1['val_accuracy'], x=range(len(h1['val_accuracy'])), label="Validation Accuracy", color="blue")
    plt.xlabel("Epochs")
    plt.ylabel("Accuracy")
    plt.title(f"Accuracy: Train = {final_train_acc:.4f}, Val = {final_val_acc:.4f}")
    plt.legend()

    # Plot Loss
    plt.subplot(1, 2, 2)
    sns.lineplot(y=h1['loss'], x=range(len(h1['loss'])), label="Training Loss", color="orange")
    sns.lineplot(y=h1['val_loss'], x=range(len(h1['val_loss'])), label="Validation Loss", color="red")
    plt.xlabel("Epochs")
    plt.ylabel("Loss")
    plt.title(f"Loss: Train = {final_train_loss:.4f}, Val = {final_val_loss:.4f}")
    plt.legend()

    plt.show()
InΒ [Β ]:
def plot_confusion_matrix(model, test_generator, class_names=None, palette='Set2'):
    """
    Plots a confusion matrix heatmap for a model and a generator-based test dataset.

    Parameters:
    - model: Trained Keras model
    - test_generator: Generator for test data
    - class_names: List of class names (optional)
    - palette: Color palette for heatmap (default: 'Blues')

    Displays:
    - Heatmap of confusion matrix
    """
    # Get predictions and true labels
    y_pred = model.predict(test_generator)
    y_pred_labels = np.argmax(y_pred, axis=1)
    y_true = test_generator.classes  # true labels from generator

    # Confusion matrix
    cm = confusion_matrix(y_true, y_pred_labels)

    if class_names is None and hasattr(test_generator, 'class_indices'):
        class_names = list(test_generator.class_indices.keys())

    plt.figure(figsize=(10, 7))
    sns.heatmap(cm, annot=True, fmt='d', cmap=palette,
                xticklabels=class_names, yticklabels=class_names)
    plt.xlabel('Predicted')
    plt.ylabel('Actual')
    plt.title('Confusion Matrix')
    plt.show()
InΒ [Β ]:
def visualize_predictions(model, test_generator, class_names=None, num_images=12):
    """
    Visualize predictions made by a model on a test generator.

    Parameters:
    - model: Trained Keras model.
    - test_generator: ImageDataGenerator with shuffle=False.
    - class_names: List of class names (optional). If None, will use from generator.
    - num_images: Number of images to display.
    """
    # Get batch of images and labels
    images, labels = next(iter(test_generator))
    predictions = model.predict(images)
    pred_labels = np.argmax(predictions, axis=1)
    true_labels = np.argmax(labels, axis=1)

    # Use class names from generator if not provided
    if class_names is None:
        class_names = list(test_generator.class_indices.keys())

    plt.figure(figsize=(15, 10))
    for i in range(min(num_images, len(images))):
        plt.subplot(3, 4, i + 1)
        plt.imshow(images[i])
        plt.axis('off')
        plt.title(f"True: {class_names[true_labels[i]]}\nPred: {class_names[pred_labels[i]]}",
                  color='green' if pred_labels[i] == true_labels[i] else 'red')
    plt.tight_layout()
    plt.show()
InΒ [Β ]:
def generate_classification_report(model, test_generator):
    """
    Generate and print a classification report using a Keras model and an image data generator.

    Parameters:
    - model: Trained Keras model
    - test_generator: ImageDataGenerator.flow_from_directory object, must be created with shuffle=False
    """
    # Get true labels
    y_true = test_generator.classes
    class_labels = list(test_generator.class_indices.keys())

    # Predict probabilities
    y_pred_probs = model.predict(test_generator, verbose=1)

    # Convert to predicted class indices
    y_pred = np.argmax(y_pred_probs, axis=1)

    # Print classification report
    print(classification_report(y_true, y_pred, target_names=class_labels))

With Default Train Test Set (without Balanced)ΒΆ

InΒ [Β ]:
# Define checkpint eraly stopping call back
checkpointer, earlystopping, reduceLR=get_callbacks(filepath='model_2.weights.h5')
InΒ [Β ]:
# create model
K.clear_session()
cnn_model=build_basic_cnn((128, 128, 3), 10)
InΒ [Β ]:
cnn_model.summary()

Model: "sequential"

┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┑━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
β”‚ conv2d (Conv2D)                 β”‚ (None, 126, 126, 32)   β”‚           896 β”‚
β”œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”Όβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”Όβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€
β”‚ max_pooling2d (MaxPooling2D)    β”‚ (None, 63, 63, 32)     β”‚             0 β”‚
β”œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”Όβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”Όβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€
β”‚ conv2d_1 (Conv2D)               β”‚ (None, 61, 61, 64)     β”‚        18,496 β”‚
β”œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”Όβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”Όβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€
β”‚ max_pooling2d_1 (MaxPooling2D)  β”‚ (None, 30, 30, 64)     β”‚             0 β”‚
β”œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”Όβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”Όβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€
β”‚ conv2d_2 (Conv2D)               β”‚ (None, 28, 28, 128)    β”‚        73,856 β”‚
β”œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”Όβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”Όβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€
β”‚ max_pooling2d_2 (MaxPooling2D)  β”‚ (None, 14, 14, 128)    β”‚             0 β”‚
β”œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”Όβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”Όβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€
β”‚ flatten (Flatten)               β”‚ (None, 25088)          β”‚             0 β”‚
β”œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”Όβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”Όβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€
β”‚ dense (Dense)                   β”‚ (None, 128)            β”‚     3,211,392 β”‚
β”œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”Όβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”Όβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€
β”‚ dropout (Dropout)               β”‚ (None, 128)            β”‚             0 β”‚
β”œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”Όβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”Όβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€
β”‚ dense_1 (Dense)                 β”‚ (None, 10)             β”‚         1,290 β”‚
β””β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”΄β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”΄β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”˜

 Total params: 3,305,930 (12.61 MB)

 Trainable params: 3,305,930 (12.61 MB)

 Non-trainable params: 0 (0.00 B)
InΒ [Β ]:
cnn_model.compile(
    optimizer='adam',
    loss='categorical_crossentropy',
    metrics=['accuracy']
)
InΒ [Β ]:
history = cnn_model.fit(
    X_train,
    y=y_train,
    epochs=50,
    validation_data=(X_valid, y_valid),
    callbacks=[checkpointer, reduceLR, earlystopping]

    )

Epoch 1/50
15/15 ━━━━━━━━━━━━━━━━━━━━ 0s 242ms/step - accuracy: 0.1012 - loss: 2.4096
Epoch 1: val_loss did not improve from 1.63336
15/15 ━━━━━━━━━━━━━━━━━━━━ 8s 327ms/step - accuracy: 0.1026 - loss: 2.4065 - val_accuracy: 0.2143 - val_loss: 2.2699 - learning_rate: 0.0010
Epoch 2/50
13/15 ━━━━━━━━━━━━━━━━━━━━ 0s 24ms/step - accuracy: 0.1540 - loss: 2.2638
Epoch 2: val_loss did not improve from 1.63336
15/15 ━━━━━━━━━━━━━━━━━━━━ 0s 29ms/step - accuracy: 0.1594 - loss: 2.2615 - val_accuracy: 0.1786 - val_loss: 2.1883 - learning_rate: 0.0010
Epoch 3/50
14/15 ━━━━━━━━━━━━━━━━━━━━ 0s 17ms/step - accuracy: 0.2272 - loss: 2.1487
Epoch 3: val_loss did not improve from 1.63336
15/15 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.2259 - loss: 2.1507 - val_accuracy: 0.2143 - val_loss: 2.1650 - learning_rate: 0.0010
Epoch 4/50
15/15 ━━━━━━━━━━━━━━━━━━━━ 0s 16ms/step - accuracy: 0.2670 - loss: 2.0638
Epoch 4: val_loss did not improve from 1.63336
15/15 ━━━━━━━━━━━━━━━━━━━━ 1s 23ms/step - accuracy: 0.2657 - loss: 2.0654 - val_accuracy: 0.1964 - val_loss: 2.1575 - learning_rate: 0.0010
Epoch 5/50
14/15 ━━━━━━━━━━━━━━━━━━━━ 0s 17ms/step - accuracy: 0.3067 - loss: 2.0220
Epoch 5: val_loss did not improve from 1.63336
15/15 ━━━━━━━━━━━━━━━━━━━━ 0s 23ms/step - accuracy: 0.3054 - loss: 2.0214 - val_accuracy: 0.1250 - val_loss: 2.2734 - learning_rate: 0.0010
Epoch 6/50
13/15 ━━━━━━━━━━━━━━━━━━━━ 0s 17ms/step - accuracy: 0.2502 - loss: 2.0495
Epoch 6: val_loss did not improve from 1.63336
15/15 ━━━━━━━━━━━━━━━━━━━━ 1s 25ms/step - accuracy: 0.2597 - loss: 2.0407 - val_accuracy: 0.2500 - val_loss: 2.1587 - learning_rate: 0.0010
Epoch 7/50
14/15 ━━━━━━━━━━━━━━━━━━━━ 0s 17ms/step - accuracy: 0.3443 - loss: 1.8332
Epoch 7: val_loss did not improve from 1.63336
15/15 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.3469 - loss: 1.8325 - val_accuracy: 0.1250 - val_loss: 2.1677 - learning_rate: 0.0010
Epoch 8/50
14/15 ━━━━━━━━━━━━━━━━━━━━ 0s 17ms/step - accuracy: 0.4462 - loss: 1.6373
Epoch 8: val_loss did not improve from 1.63336
15/15 ━━━━━━━━━━━━━━━━━━━━ 0s 23ms/step - accuracy: 0.4452 - loss: 1.6352 - val_accuracy: 0.2143 - val_loss: 2.2866 - learning_rate: 0.0010
Epoch 9/50
13/15 ━━━━━━━━━━━━━━━━━━━━ 0s 17ms/step - accuracy: 0.4688 - loss: 1.5378
Epoch 9: val_loss did not improve from 1.63336
15/15 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.4745 - loss: 1.5261 - val_accuracy: 0.2321 - val_loss: 2.4977 - learning_rate: 0.0010
Epoch 10/50
14/15 ━━━━━━━━━━━━━━━━━━━━ 0s 17ms/step - accuracy: 0.5545 - loss: 1.1759
Epoch 10: val_loss did not improve from 1.63336
15/15 ━━━━━━━━━━━━━━━━━━━━ 0s 23ms/step - accuracy: 0.5571 - loss: 1.1779 - val_accuracy: 0.1964 - val_loss: 2.5918 - learning_rate: 0.0010
Epoch 11/50
13/15 ━━━━━━━━━━━━━━━━━━━━ 0s 17ms/step - accuracy: 0.7186 - loss: 0.9608
Epoch 11: val_loss did not improve from 1.63336
15/15 ━━━━━━━━━━━━━━━━━━━━ 0s 23ms/step - accuracy: 0.7145 - loss: 0.9667 - val_accuracy: 0.2143 - val_loss: 2.8094 - learning_rate: 0.0010
Epoch 12/50
14/15 ━━━━━━━━━━━━━━━━━━━━ 0s 17ms/step - accuracy: 0.7347 - loss: 0.8325
Epoch 12: val_loss did not improve from 1.63336
15/15 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.7380 - loss: 0.8262 - val_accuracy: 0.2321 - val_loss: 3.2011 - learning_rate: 0.0010
Epoch 13/50
13/15 ━━━━━━━━━━━━━━━━━━━━ 0s 18ms/step - accuracy: 0.7625 - loss: 0.6887
Epoch 13: val_loss did not improve from 1.63336
15/15 ━━━━━━━━━━━━━━━━━━━━ 1s 24ms/step - accuracy: 0.7693 - loss: 0.6780 - val_accuracy: 0.2321 - val_loss: 3.4972 - learning_rate: 0.0010
Epoch 14/50
13/15 ━━━━━━━━━━━━━━━━━━━━ 0s 18ms/step - accuracy: 0.8250 - loss: 0.5523
Epoch 14: val_loss did not improve from 1.63336
15/15 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.8241 - loss: 0.5529 - val_accuracy: 0.1786 - val_loss: 3.5632 - learning_rate: 0.0010
Epoch 15/50
14/15 ━━━━━━━━━━━━━━━━━━━━ 0s 17ms/step - accuracy: 0.8831 - loss: 0.4401
Epoch 15: val_loss did not improve from 1.63336
15/15 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.8809 - loss: 0.4420 - val_accuracy: 0.1964 - val_loss: 3.9332 - learning_rate: 5.0000e-04
Epoch 16/50
15/15 ━━━━━━━━━━━━━━━━━━━━ 0s 16ms/step - accuracy: 0.8856 - loss: 0.3610
Epoch 16: val_loss did not improve from 1.63336
15/15 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.8853 - loss: 0.3605 - val_accuracy: 0.1964 - val_loss: 4.1603 - learning_rate: 5.0000e-04
Epoch 17/50
13/15 ━━━━━━━━━━━━━━━━━━━━ 0s 19ms/step - accuracy: 0.8698 - loss: 0.3470
Epoch 17: val_loss did not improve from 1.63336
15/15 ━━━━━━━━━━━━━━━━━━━━ 1s 26ms/step - accuracy: 0.8738 - loss: 0.3441 - val_accuracy: 0.1786 - val_loss: 3.9422 - learning_rate: 5.0000e-04
Epoch 18/50
13/15 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.9403 - loss: 0.2336
Epoch 18: val_loss did not improve from 1.63336
15/15 ━━━━━━━━━━━━━━━━━━━━ 1s 48ms/step - accuracy: 0.9386 - loss: 0.2336 - val_accuracy: 0.2143 - val_loss: 4.2191 - learning_rate: 5.0000e-04
Epoch 19/50
15/15 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.9599 - loss: 0.1735
Epoch 19: val_loss did not improve from 1.63336
15/15 ━━━━━━━━━━━━━━━━━━━━ 1s 42ms/step - accuracy: 0.9592 - loss: 0.1745 - val_accuracy: 0.2143 - val_loss: 4.6190 - learning_rate: 5.0000e-04
Epoch 20/50
14/15 ━━━━━━━━━━━━━━━━━━━━ 0s 26ms/step - accuracy: 0.9555 - loss: 0.1569
Epoch 20: val_loss did not improve from 1.63336
15/15 ━━━━━━━━━━━━━━━━━━━━ 1s 39ms/step - accuracy: 0.9561 - loss: 0.1578 - val_accuracy: 0.1786 - val_loss: 4.7255 - learning_rate: 5.0000e-04
Epoch 21/50
13/15 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.9312 - loss: 0.1951
Epoch 21: val_loss did not improve from 1.63336
15/15 ━━━━━━━━━━━━━━━━━━━━ 1s 45ms/step - accuracy: 0.9329 - loss: 0.1964 - val_accuracy: 0.1786 - val_loss: 4.4341 - learning_rate: 5.0000e-04
Epoch 22/50
15/15 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.9624 - loss: 0.1533
Epoch 22: val_loss did not improve from 1.63336
15/15 ━━━━━━━━━━━━━━━━━━━━ 1s 40ms/step - accuracy: 0.9624 - loss: 0.1536 - val_accuracy: 0.1964 - val_loss: 4.0692 - learning_rate: 5.0000e-04
Epoch 23/50
13/15 ━━━━━━━━━━━━━━━━━━━━ 0s 19ms/step - accuracy: 0.9518 - loss: 0.1254
Epoch 23: val_loss did not improve from 1.63336
15/15 ━━━━━━━━━━━━━━━━━━━━ 1s 37ms/step - accuracy: 0.9525 - loss: 0.1302 - val_accuracy: 0.2143 - val_loss: 4.6879 - learning_rate: 5.0000e-04

Model is overfitting

InΒ [Β ]:
plot_model_accuracy(history,cnn_model)
No description has been provided for this image

Observation:ΒΆ

The training and validation curve is diverging .Not a good model for further analysis

With Image Generator And balanced Train Set (Adam)ΒΆ

InΒ [Β ]:
# check point and eraly stopping call back
checkpointer, earlystopping, reduceLR=get_callbacks(filepath='model_2.weights.h5')
InΒ [Β ]:
# create basic model
K.clear_session()
model_2=build_basic_cnn((128, 128, 3), 10)

/usr/local/lib/python3.11/dist-packages/keras/src/layers/convolutional/base_conv.py:107: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.
  super().__init__(activity_regularizer=activity_regularizer, **kwargs)
InΒ [Β ]:
# compile model
model_2.compile(
    optimizer='adam',
    loss='categorical_crossentropy',
    metrics=['accuracy']
)
InΒ [Β ]:
# Starting training
history = model_2.fit(
    train_gen,
    epochs=50,
    validation_data=valid_gen,
    callbacks=[checkpointer, reduceLR, earlystopping]
    )

/usr/local/lib/python3.11/dist-packages/keras/src/trainers/data_adapters/py_dataset_adapter.py:121: UserWarning: Your `PyDataset` class should call `super().__init__(**kwargs)` in its constructor. `**kwargs` can include `workers`, `use_multiprocessing`, `max_queue_size`. Do not pass these arguments to `fit()`, as they will be ignored.
  self._warn_if_super_not_called()

Epoch 1/50
14/14 ━━━━━━━━━━━━━━━━━━━━ 0s 478ms/step - accuracy: 0.0865 - loss: 2.3364
Epoch 1: val_loss improved from inf to 2.29801, saving model to model_2.weights.h5
14/14 ━━━━━━━━━━━━━━━━━━━━ 14s 631ms/step - accuracy: 0.0870 - loss: 2.3356 - val_accuracy: 0.1607 - val_loss: 2.2980 - learning_rate: 0.0010
Epoch 2/50
14/14 ━━━━━━━━━━━━━━━━━━━━ 0s 179ms/step - accuracy: 0.1392 - loss: 2.2951
Epoch 2: val_loss improved from 2.29801 to 2.27082, saving model to model_2.weights.h5
14/14 ━━━━━━━━━━━━━━━━━━━━ 3s 203ms/step - accuracy: 0.1395 - loss: 2.2951 - val_accuracy: 0.1964 - val_loss: 2.2708 - learning_rate: 0.0010
Epoch 3/50
14/14 ━━━━━━━━━━━━━━━━━━━━ 0s 182ms/step - accuracy: 0.1855 - loss: 2.2526
Epoch 3: val_loss improved from 2.27082 to 2.19070, saving model to model_2.weights.h5
14/14 ━━━━━━━━━━━━━━━━━━━━ 3s 204ms/step - accuracy: 0.1848 - loss: 2.2515 - val_accuracy: 0.1786 - val_loss: 2.1907 - learning_rate: 0.0010
Epoch 4/50
14/14 ━━━━━━━━━━━━━━━━━━━━ 0s 183ms/step - accuracy: 0.1795 - loss: 2.2026
Epoch 4: val_loss improved from 2.19070 to 2.16503, saving model to model_2.weights.h5
14/14 ━━━━━━━━━━━━━━━━━━━━ 3s 214ms/step - accuracy: 0.1802 - loss: 2.2013 - val_accuracy: 0.2143 - val_loss: 2.1650 - learning_rate: 0.0010
Epoch 5/50
14/14 ━━━━━━━━━━━━━━━━━━━━ 0s 253ms/step - accuracy: 0.2301 - loss: 2.1607
Epoch 5: val_loss did not improve from 2.16503
14/14 ━━━━━━━━━━━━━━━━━━━━ 4s 266ms/step - accuracy: 0.2295 - loss: 2.1603 - val_accuracy: 0.2143 - val_loss: 2.1890 - learning_rate: 0.0010
Epoch 6/50
14/14 ━━━━━━━━━━━━━━━━━━━━ 0s 183ms/step - accuracy: 0.2213 - loss: 2.0869
Epoch 6: val_loss did not improve from 2.16503
14/14 ━━━━━━━━━━━━━━━━━━━━ 3s 197ms/step - accuracy: 0.2214 - loss: 2.0878 - val_accuracy: 0.1429 - val_loss: 2.3165 - learning_rate: 0.0010
Epoch 7/50
14/14 ━━━━━━━━━━━━━━━━━━━━ 0s 178ms/step - accuracy: 0.1907 - loss: 2.0870
Epoch 7: val_loss improved from 2.16503 to 2.15649, saving model to model_2.weights.h5
14/14 ━━━━━━━━━━━━━━━━━━━━ 3s 199ms/step - accuracy: 0.1923 - loss: 2.0861 - val_accuracy: 0.1964 - val_loss: 2.1565 - learning_rate: 0.0010
Epoch 8/50
14/14 ━━━━━━━━━━━━━━━━━━━━ 0s 180ms/step - accuracy: 0.2282 - loss: 2.0676
Epoch 8: val_loss did not improve from 2.15649
14/14 ━━━━━━━━━━━━━━━━━━━━ 3s 193ms/step - accuracy: 0.2305 - loss: 2.0648 - val_accuracy: 0.2321 - val_loss: 2.2249 - learning_rate: 0.0010
Epoch 9/50
14/14 ━━━━━━━━━━━━━━━━━━━━ 0s 259ms/step - accuracy: 0.2470 - loss: 2.0093
Epoch 9: val_loss did not improve from 2.15649
14/14 ━━━━━━━━━━━━━━━━━━━━ 4s 278ms/step - accuracy: 0.2472 - loss: 2.0101 - val_accuracy: 0.2321 - val_loss: 2.1629 - learning_rate: 0.0010
Epoch 10/50
14/14 ━━━━━━━━━━━━━━━━━━━━ 0s 181ms/step - accuracy: 0.2513 - loss: 1.9634
Epoch 10: val_loss improved from 2.15649 to 2.04943, saving model to model_2.weights.h5
14/14 ━━━━━━━━━━━━━━━━━━━━ 4s 203ms/step - accuracy: 0.2524 - loss: 1.9639 - val_accuracy: 0.2857 - val_loss: 2.0494 - learning_rate: 0.0010
Epoch 11/50
14/14 ━━━━━━━━━━━━━━━━━━━━ 0s 179ms/step - accuracy: 0.3193 - loss: 1.9118
Epoch 11: val_loss did not improve from 2.04943
14/14 ━━━━━━━━━━━━━━━━━━━━ 3s 194ms/step - accuracy: 0.3185 - loss: 1.9124 - val_accuracy: 0.2679 - val_loss: 2.0741 - learning_rate: 0.0010
Epoch 12/50
14/14 ━━━━━━━━━━━━━━━━━━━━ 0s 257ms/step - accuracy: 0.3131 - loss: 1.9116
Epoch 12: val_loss did not improve from 2.04943
14/14 ━━━━━━━━━━━━━━━━━━━━ 6s 282ms/step - accuracy: 0.3129 - loss: 1.9139 - val_accuracy: 0.2679 - val_loss: 2.0538 - learning_rate: 0.0010
Epoch 13/50
14/14 ━━━━━━━━━━━━━━━━━━━━ 0s 183ms/step - accuracy: 0.2928 - loss: 1.9432
Epoch 13: val_loss improved from 2.04943 to 2.03074, saving model to model_2.weights.h5
14/14 ━━━━━━━━━━━━━━━━━━━━ 3s 204ms/step - accuracy: 0.2944 - loss: 1.9411 - val_accuracy: 0.3393 - val_loss: 2.0307 - learning_rate: 0.0010
Epoch 14/50
14/14 ━━━━━━━━━━━━━━━━━━━━ 0s 180ms/step - accuracy: 0.2859 - loss: 1.9070
Epoch 14: val_loss did not improve from 2.03074
14/14 ━━━━━━━━━━━━━━━━━━━━ 5s 193ms/step - accuracy: 0.2851 - loss: 1.9068 - val_accuracy: 0.2679 - val_loss: 2.1372 - learning_rate: 0.0010
Epoch 15/50
14/14 ━━━━━━━━━━━━━━━━━━━━ 0s 182ms/step - accuracy: 0.3066 - loss: 1.8571
Epoch 15: val_loss did not improve from 2.03074
14/14 ━━━━━━━━━━━━━━━━━━━━ 3s 200ms/step - accuracy: 0.3074 - loss: 1.8564 - val_accuracy: 0.3393 - val_loss: 2.1604 - learning_rate: 0.0010
Epoch 16/50
14/14 ━━━━━━━━━━━━━━━━━━━━ 0s 255ms/step - accuracy: 0.3891 - loss: 1.8271
Epoch 16: val_loss did not improve from 2.03074
14/14 ━━━━━━━━━━━━━━━━━━━━ 4s 270ms/step - accuracy: 0.3869 - loss: 1.8288 - val_accuracy: 0.3393 - val_loss: 2.1290 - learning_rate: 0.0010
Epoch 17/50
14/14 ━━━━━━━━━━━━━━━━━━━━ 0s 177ms/step - accuracy: 0.3130 - loss: 1.8523
Epoch 17: val_loss did not improve from 2.03074
14/14 ━━━━━━━━━━━━━━━━━━━━ 3s 190ms/step - accuracy: 0.3150 - loss: 1.8505 - val_accuracy: 0.3214 - val_loss: 2.0610 - learning_rate: 0.0010
Epoch 18/50
14/14 ━━━━━━━━━━━━━━━━━━━━ 0s 183ms/step - accuracy: 0.2994 - loss: 1.8438
Epoch 18: val_loss did not improve from 2.03074
14/14 ━━━━━━━━━━━━━━━━━━━━ 3s 196ms/step - accuracy: 0.3002 - loss: 1.8440 - val_accuracy: 0.3214 - val_loss: 2.1416 - learning_rate: 0.0010
Epoch 19/50
14/14 ━━━━━━━━━━━━━━━━━━━━ 0s 179ms/step - accuracy: 0.3271 - loss: 1.8279
Epoch 19: val_loss did not improve from 2.03074
14/14 ━━━━━━━━━━━━━━━━━━━━ 3s 192ms/step - accuracy: 0.3290 - loss: 1.8280 - val_accuracy: 0.3393 - val_loss: 2.0837 - learning_rate: 0.0010
Epoch 20/50
14/14 ━━━━━━━━━━━━━━━━━━━━ 0s 253ms/step - accuracy: 0.3517 - loss: 1.7238
Epoch 20: val_loss did not improve from 2.03074
14/14 ━━━━━━━━━━━━━━━━━━━━ 4s 271ms/step - accuracy: 0.3508 - loss: 1.7265 - val_accuracy: 0.3214 - val_loss: 2.1897 - learning_rate: 0.0010
Epoch 21/50
14/14 ━━━━━━━━━━━━━━━━━━━━ 0s 189ms/step - accuracy: 0.3517 - loss: 1.7843
Epoch 21: val_loss did not improve from 2.03074
14/14 ━━━━━━━━━━━━━━━━━━━━ 3s 202ms/step - accuracy: 0.3495 - loss: 1.7913 - val_accuracy: 0.3571 - val_loss: 2.0574 - learning_rate: 0.0010
Epoch 22/50
14/14 ━━━━━━━━━━━━━━━━━━━━ 0s 178ms/step - accuracy: 0.3255 - loss: 1.7916
Epoch 22: val_loss did not improve from 2.03074
14/14 ━━━━━━━━━━━━━━━━━━━━ 3s 191ms/step - accuracy: 0.3264 - loss: 1.7923 - val_accuracy: 0.2857 - val_loss: 2.0939 - learning_rate: 0.0010
Epoch 23/50
14/14 ━━━━━━━━━━━━━━━━━━━━ 0s 181ms/step - accuracy: 0.3599 - loss: 1.7747
Epoch 23: val_loss did not improve from 2.03074
14/14 ━━━━━━━━━━━━━━━━━━━━ 3s 195ms/step - accuracy: 0.3603 - loss: 1.7728 - val_accuracy: 0.2857 - val_loss: 2.2142 - learning_rate: 0.0010
Epoch 24/50
14/14 ━━━━━━━━━━━━━━━━━━━━ 0s 207ms/step - accuracy: 0.3770 - loss: 1.7190
Epoch 24: val_loss did not improve from 2.03074
14/14 ━━━━━━━━━━━━━━━━━━━━ 3s 225ms/step - accuracy: 0.3782 - loss: 1.7195 - val_accuracy: 0.2857 - val_loss: 2.2082 - learning_rate: 5.0000e-04
Epoch 25/50
14/14 ━━━━━━━━━━━━━━━━━━━━ 0s 176ms/step - accuracy: 0.3441 - loss: 1.7958
Epoch 25: val_loss did not improve from 2.03074
14/14 ━━━━━━━━━━━━━━━━━━━━ 5s 189ms/step - accuracy: 0.3447 - loss: 1.7916 - val_accuracy: 0.3036 - val_loss: 2.1900 - learning_rate: 5.0000e-04
Epoch 26/50
14/14 ━━━━━━━━━━━━━━━━━━━━ 0s 184ms/step - accuracy: 0.3958 - loss: 1.6648
Epoch 26: val_loss improved from 2.03074 to 2.00356, saving model to model_2.weights.h5
14/14 ━━━━━━━━━━━━━━━━━━━━ 3s 205ms/step - accuracy: 0.3968 - loss: 1.6628 - val_accuracy: 0.4107 - val_loss: 2.0036 - learning_rate: 5.0000e-04
Epoch 27/50
14/14 ━━━━━━━━━━━━━━━━━━━━ 0s 179ms/step - accuracy: 0.4179 - loss: 1.5828
Epoch 27: val_loss improved from 2.00356 to 1.97448, saving model to model_2.weights.h5
14/14 ━━━━━━━━━━━━━━━━━━━━ 3s 201ms/step - accuracy: 0.4168 - loss: 1.5840 - val_accuracy: 0.4107 - val_loss: 1.9745 - learning_rate: 5.0000e-04
Epoch 28/50
14/14 ━━━━━━━━━━━━━━━━━━━━ 0s 252ms/step - accuracy: 0.4444 - loss: 1.5592
Epoch 28: val_loss did not improve from 1.97448
14/14 ━━━━━━━━━━━━━━━━━━━━ 4s 277ms/step - accuracy: 0.4443 - loss: 1.5590 - val_accuracy: 0.3393 - val_loss: 2.0945 - learning_rate: 5.0000e-04
Epoch 29/50
14/14 ━━━━━━━━━━━━━━━━━━━━ 0s 183ms/step - accuracy: 0.4195 - loss: 1.6237
Epoch 29: val_loss did not improve from 1.97448
14/14 ━━━━━━━━━━━━━━━━━━━━ 3s 196ms/step - accuracy: 0.4193 - loss: 1.6222 - val_accuracy: 0.4286 - val_loss: 1.9803 - learning_rate: 5.0000e-04
Epoch 30/50
14/14 ━━━━━━━━━━━━━━━━━━━━ 0s 181ms/step - accuracy: 0.4430 - loss: 1.5234
Epoch 30: val_loss did not improve from 1.97448
14/14 ━━━━━━━━━━━━━━━━━━━━ 3s 194ms/step - accuracy: 0.4438 - loss: 1.5249 - val_accuracy: 0.2857 - val_loss: 2.1961 - learning_rate: 5.0000e-04
Epoch 31/50
14/14 ━━━━━━━━━━━━━━━━━━━━ 0s 179ms/step - accuracy: 0.4131 - loss: 1.5838
Epoch 31: val_loss did not improve from 1.97448
14/14 ━━━━━━━━━━━━━━━━━━━━ 3s 192ms/step - accuracy: 0.4151 - loss: 1.5815 - val_accuracy: 0.4464 - val_loss: 2.1299 - learning_rate: 5.0000e-04
Epoch 32/50
14/14 ━━━━━━━━━━━━━━━━━━━━ 0s 192ms/step - accuracy: 0.3614 - loss: 1.6509
Epoch 32: val_loss did not improve from 1.97448
14/14 ━━━━━━━━━━━━━━━━━━━━ 3s 211ms/step - accuracy: 0.3651 - loss: 1.6480 - val_accuracy: 0.3571 - val_loss: 2.2241 - learning_rate: 5.0000e-04
Epoch 33/50
14/14 ━━━━━━━━━━━━━━━━━━━━ 0s 286ms/step - accuracy: 0.4299 - loss: 1.5490
Epoch 33: val_loss did not improve from 1.97448
14/14 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 0.4304 - loss: 1.5490 - val_accuracy: 0.3750 - val_loss: 2.1025 - learning_rate: 5.0000e-04
Epoch 34/50
14/14 ━━━━━━━━━━━━━━━━━━━━ 0s 179ms/step - accuracy: 0.4411 - loss: 1.5088
Epoch 34: val_loss did not improve from 1.97448
14/14 ━━━━━━━━━━━━━━━━━━━━ 3s 193ms/step - accuracy: 0.4412 - loss: 1.5101 - val_accuracy: 0.4107 - val_loss: 2.0748 - learning_rate: 5.0000e-04
Epoch 35/50
14/14 ━━━━━━━━━━━━━━━━━━━━ 0s 179ms/step - accuracy: 0.4549 - loss: 1.5297
Epoch 35: val_loss did not improve from 1.97448
14/14 ━━━━━━━━━━━━━━━━━━━━ 3s 192ms/step - accuracy: 0.4558 - loss: 1.5293 - val_accuracy: 0.4286 - val_loss: 2.0844 - learning_rate: 5.0000e-04
Epoch 36/50
14/14 ━━━━━━━━━━━━━━━━━━━━ 0s 180ms/step - accuracy: 0.4793 - loss: 1.5011
Epoch 36: val_loss did not improve from 1.97448
14/14 ━━━━━━━━━━━━━━━━━━━━ 3s 192ms/step - accuracy: 0.4801 - loss: 1.5006 - val_accuracy: 0.3929 - val_loss: 2.1271 - learning_rate: 5.0000e-04
Epoch 37/50
14/14 ━━━━━━━━━━━━━━━━━━━━ 0s 273ms/step - accuracy: 0.5140 - loss: 1.4248
Epoch 37: val_loss did not improve from 1.97448
14/14 ━━━━━━━━━━━━━━━━━━━━ 4s 286ms/step - accuracy: 0.5120 - loss: 1.4272 - val_accuracy: 0.4286 - val_loss: 2.0606 - learning_rate: 5.0000e-04
Epoch 38/50
14/14 ━━━━━━━━━━━━━━━━━━━━ 0s 183ms/step - accuracy: 0.4727 - loss: 1.5180
Epoch 38: val_loss did not improve from 1.97448
14/14 ━━━━━━━━━━━━━━━━━━━━ 3s 196ms/step - accuracy: 0.4708 - loss: 1.5183 - val_accuracy: 0.4107 - val_loss: 2.0587 - learning_rate: 2.5000e-04
Epoch 39/50
14/14 ━━━━━━━━━━━━━━━━━━━━ 0s 180ms/step - accuracy: 0.4730 - loss: 1.4134
Epoch 39: val_loss did not improve from 1.97448
14/14 ━━━━━━━━━━━━━━━━━━━━ 5s 194ms/step - accuracy: 0.4727 - loss: 1.4160 - val_accuracy: 0.4464 - val_loss: 2.0646 - learning_rate: 2.5000e-04
Epoch 40/50
14/14 ━━━━━━━━━━━━━━━━━━━━ 0s 207ms/step - accuracy: 0.4515 - loss: 1.4501
Epoch 40: val_loss did not improve from 1.97448
14/14 ━━━━━━━━━━━━━━━━━━━━ 3s 232ms/step - accuracy: 0.4540 - loss: 1.4467 - val_accuracy: 0.4464 - val_loss: 1.9971 - learning_rate: 2.5000e-04
Epoch 41/50
14/14 ━━━━━━━━━━━━━━━━━━━━ 0s 235ms/step - accuracy: 0.4626 - loss: 1.4628
Epoch 41: val_loss did not improve from 1.97448
14/14 ━━━━━━━━━━━━━━━━━━━━ 4s 247ms/step - accuracy: 0.4630 - loss: 1.4632 - val_accuracy: 0.4286 - val_loss: 2.0065 - learning_rate: 2.5000e-04
Epoch 42/50
14/14 ━━━━━━━━━━━━━━━━━━━━ 0s 184ms/step - accuracy: 0.5062 - loss: 1.3347
Epoch 42: val_loss did not improve from 1.97448
14/14 ━━━━━━━━━━━━━━━━━━━━ 4s 197ms/step - accuracy: 0.5049 - loss: 1.3391 - val_accuracy: 0.4643 - val_loss: 2.0041 - learning_rate: 2.5000e-04
Epoch 43/50
14/14 ━━━━━━━━━━━━━━━━━━━━ 0s 178ms/step - accuracy: 0.5274 - loss: 1.3911
Epoch 43: val_loss did not improve from 1.97448
14/14 ━━━━━━━━━━━━━━━━━━━━ 3s 190ms/step - accuracy: 0.5242 - loss: 1.3968 - val_accuracy: 0.4107 - val_loss: 2.0694 - learning_rate: 2.5000e-04
Epoch 44/50
14/14 ━━━━━━━━━━━━━━━━━━━━ 0s 233ms/step - accuracy: 0.5570 - loss: 1.3475
Epoch 44: val_loss did not improve from 1.97448
14/14 ━━━━━━━━━━━━━━━━━━━━ 3s 251ms/step - accuracy: 0.5554 - loss: 1.3524 - val_accuracy: 0.3750 - val_loss: 2.2388 - learning_rate: 2.5000e-04
Epoch 45/50
14/14 ━━━━━━━━━━━━━━━━━━━━ 0s 211ms/step - accuracy: 0.4696 - loss: 1.4701
Epoch 45: val_loss did not improve from 1.97448
14/14 ━━━━━━━━━━━━━━━━━━━━ 3s 223ms/step - accuracy: 0.4713 - loss: 1.4672 - val_accuracy: 0.5000 - val_loss: 2.0221 - learning_rate: 2.5000e-04
Epoch 46/50
14/14 ━━━━━━━━━━━━━━━━━━━━ 0s 182ms/step - accuracy: 0.5258 - loss: 1.3349
Epoch 46: val_loss improved from 1.97448 to 1.97421, saving model to model_2.weights.h5
14/14 ━━━━━━━━━━━━━━━━━━━━ 3s 203ms/step - accuracy: 0.5257 - loss: 1.3367 - val_accuracy: 0.5000 - val_loss: 1.9742 - learning_rate: 2.5000e-04
Epoch 47/50
14/14 ━━━━━━━━━━━━━━━━━━━━ 0s 179ms/step - accuracy: 0.5265 - loss: 1.4327
Epoch 47: val_loss did not improve from 1.97421
14/14 ━━━━━━━━━━━━━━━━━━━━ 3s 192ms/step - accuracy: 0.5262 - loss: 1.4298 - val_accuracy: 0.4107 - val_loss: 2.1361 - learning_rate: 2.5000e-04
InΒ [Β ]:
plot_model_accuracy(history,model_2)
No description has been provided for this image
  • So much noise in training and validation
  • We can see its going up an down significantly and model training accuracy of 52% tells that model is failing to learn nuancest features
InΒ [Β ]:
model_2.evaluate(test_gen)

/usr/local/lib/python3.11/dist-packages/keras/src/trainers/data_adapters/py_dataset_adapter.py:121: UserWarning: Your `PyDataset` class should call `super().__init__(**kwargs)` in its constructor. `**kwargs` can include `workers`, `use_multiprocessing`, `max_queue_size`. Do not pass these arguments to `fit()`, as they will be ignored.
  self._warn_if_super_not_called()

2/2 ━━━━━━━━━━━━━━━━━━━━ 0s 61ms/step - accuracy: 0.5000 - loss: 1.5691
Out[Β ]:
[1.6031839847564697, 0.5]

Observations:ΒΆ

  • Model failed to generalise as training accuracy is around 52% with validation accuracy is at 41%.
  • Test acccuracy is around 50%
  • We can see both validation and train curve has noises .
InΒ [Β ]:
# SHow classfication report
generate_classification_report(model_2, test_gen)

2/2 ━━━━━━━━━━━━━━━━━━━━ 1s 309ms/step
                      precision    recall  f1-score   support

      chocolate_cake       1.00      1.00      1.00         5
        french_fries       0.50      0.62      0.56         8
           ice_cream       0.57      0.67      0.62         6
              nachos       0.33      0.20      0.25         5
          onion_ring       0.20      0.20      0.20         5
               pizza       0.55      1.00      0.71         6
              samosa       0.17      0.14      0.15         7
strawberry_shortcake       1.00      0.40      0.57         5
               tacos       0.50      0.40      0.44         5
              waffle       0.33      0.25      0.29         4

            accuracy                           0.50        56
           macro avg       0.52      0.49      0.48        56
        weighted avg       0.51      0.50      0.48        56
InΒ [Β ]:
# Show Confusion matrix
plot_confusion_matrix(model_2,test_gen,list(test_gen.class_indices.keys()))

2/2 ━━━━━━━━━━━━━━━━━━━━ 0s 65ms/step
No description has been provided for this image

Observations:ΒΆ

  • Overall Test Accuracy: 50%

  • Overall Accuracy is at 52%

  • High Performing Classes: chocolate_cake, pizza, ice_cream show good precision and recall.

  • Poorly Learned Classes: samosa, onion_ring, strawberry_shortcake β€” either completely misclassified or low recall.

Model With He normal and more layers(Adam)ΒΆ

InΒ [Β ]:
# check point and eraly stopping call back
checkpointer, earlystopping, reduceLR=get_callbacks(filepath='cnn_model_2.weights.h5')
InΒ [Β ]:
# define the model bulding from utility
K.clear_session()
cnn_model_2=build_basic_cnn_2((128, 128, 3), 10)

/usr/local/lib/python3.11/dist-packages/keras/src/layers/convolutional/base_conv.py:107: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.
  super().__init__(activity_regularizer=activity_regularizer, **kwargs)
InΒ [Β ]:
cnn_model_2.compile(
    optimizer='adam',
    loss='categorical_crossentropy',
    metrics=['accuracy']
)
InΒ [Β ]:
history = cnn_model_2.fit(
    train_gen,
    epochs=50,
    validation_data=valid_gen,
    callbacks=[checkpointer, reduceLR, earlystopping]
    )

Epoch 1/50
14/14 ━━━━━━━━━━━━━━━━━━━━ 0s 270ms/step - accuracy: 0.0770 - loss: 2.7664
Epoch 1: val_loss improved from inf to 2.28435, saving model to cnn_model_2.weights.h5
14/14 ━━━━━━━━━━━━━━━━━━━━ 8s 409ms/step - accuracy: 0.0777 - loss: 2.7506 - val_accuracy: 0.1250 - val_loss: 2.2843 - learning_rate: 0.0010
Epoch 2/50
14/14 ━━━━━━━━━━━━━━━━━━━━ 0s 339ms/step - accuracy: 0.1141 - loss: 2.2798
Epoch 2: val_loss improved from 2.28435 to 2.22756, saving model to cnn_model_2.weights.h5
14/14 ━━━━━━━━━━━━━━━━━━━━ 5s 376ms/step - accuracy: 0.1152 - loss: 2.2794 - val_accuracy: 0.1429 - val_loss: 2.2276 - learning_rate: 0.0010
Epoch 3/50
14/14 ━━━━━━━━━━━━━━━━━━━━ 0s 189ms/step - accuracy: 0.1768 - loss: 2.1937
Epoch 3: val_loss did not improve from 2.22756
14/14 ━━━━━━━━━━━━━━━━━━━━ 3s 203ms/step - accuracy: 0.1764 - loss: 2.1945 - val_accuracy: 0.1250 - val_loss: 2.2510 - learning_rate: 0.0010
Epoch 4/50
14/14 ━━━━━━━━━━━━━━━━━━━━ 0s 183ms/step - accuracy: 0.1337 - loss: 2.2320
Epoch 4: val_loss did not improve from 2.22756
14/14 ━━━━━━━━━━━━━━━━━━━━ 3s 197ms/step - accuracy: 0.1354 - loss: 2.2302 - val_accuracy: 0.1786 - val_loss: 2.2319 - learning_rate: 0.0010
Epoch 5/50
14/14 ━━━━━━━━━━━━━━━━━━━━ 0s 198ms/step - accuracy: 0.2185 - loss: 2.1637
Epoch 5: val_loss improved from 2.22756 to 2.16096, saving model to cnn_model_2.weights.h5
14/14 ━━━━━━━━━━━━━━━━━━━━ 3s 231ms/step - accuracy: 0.2183 - loss: 2.1633 - val_accuracy: 0.3036 - val_loss: 2.1610 - learning_rate: 0.0010
Epoch 6/50
14/14 ━━━━━━━━━━━━━━━━━━━━ 0s 253ms/step - accuracy: 0.2161 - loss: 2.1454
Epoch 6: val_loss improved from 2.16096 to 2.13033, saving model to cnn_model_2.weights.h5
14/14 ━━━━━━━━━━━━━━━━━━━━ 4s 276ms/step - accuracy: 0.2168 - loss: 2.1443 - val_accuracy: 0.3036 - val_loss: 2.1303 - learning_rate: 0.0010
Epoch 7/50
14/14 ━━━━━━━━━━━━━━━━━━━━ 0s 189ms/step - accuracy: 0.2142 - loss: 2.0771
Epoch 7: val_loss improved from 2.13033 to 2.11435, saving model to cnn_model_2.weights.h5
14/14 ━━━━━━━━━━━━━━━━━━━━ 3s 212ms/step - accuracy: 0.2141 - loss: 2.0776 - val_accuracy: 0.2679 - val_loss: 2.1143 - learning_rate: 0.0010
Epoch 8/50
14/14 ━━━━━━━━━━━━━━━━━━━━ 0s 184ms/step - accuracy: 0.2793 - loss: 2.0360
Epoch 8: val_loss did not improve from 2.11435
14/14 ━━━━━━━━━━━━━━━━━━━━ 3s 209ms/step - accuracy: 0.2787 - loss: 2.0371 - val_accuracy: 0.2857 - val_loss: 2.1367 - learning_rate: 0.0010
Epoch 9/50
14/14 ━━━━━━━━━━━━━━━━━━━━ 0s 185ms/step - accuracy: 0.2434 - loss: 1.9952
Epoch 9: val_loss improved from 2.11435 to 2.10751, saving model to cnn_model_2.weights.h5
14/14 ━━━━━━━━━━━━━━━━━━━━ 3s 224ms/step - accuracy: 0.2421 - loss: 1.9981 - val_accuracy: 0.2679 - val_loss: 2.1075 - learning_rate: 0.0010
Epoch 10/50
14/14 ━━━━━━━━━━━━━━━━━━━━ 0s 188ms/step - accuracy: 0.3152 - loss: 1.9892
Epoch 10: val_loss improved from 2.10751 to 1.98847, saving model to cnn_model_2.weights.h5
14/14 ━━━━━━━━━━━━━━━━━━━━ 5s 211ms/step - accuracy: 0.3148 - loss: 1.9867 - val_accuracy: 0.3393 - val_loss: 1.9885 - learning_rate: 0.0010
Epoch 11/50
14/14 ━━━━━━━━━━━━━━━━━━━━ 0s 180ms/step - accuracy: 0.2695 - loss: 1.9874
Epoch 11: val_loss did not improve from 1.98847
14/14 ━━━━━━━━━━━━━━━━━━━━ 3s 193ms/step - accuracy: 0.2684 - loss: 1.9891 - val_accuracy: 0.2679 - val_loss: 2.0332 - learning_rate: 0.0010
Epoch 12/50
14/14 ━━━━━━━━━━━━━━━━━━━━ 0s 181ms/step - accuracy: 0.2844 - loss: 1.9673
Epoch 12: val_loss did not improve from 1.98847
14/14 ━━━━━━━━━━━━━━━━━━━━ 3s 195ms/step - accuracy: 0.2842 - loss: 1.9696 - val_accuracy: 0.2679 - val_loss: 1.9988 - learning_rate: 0.0010
Epoch 13/50
14/14 ━━━━━━━━━━━━━━━━━━━━ 0s 250ms/step - accuracy: 0.3083 - loss: 1.8955
Epoch 13: val_loss did not improve from 1.98847
14/14 ━━━━━━━━━━━━━━━━━━━━ 4s 275ms/step - accuracy: 0.3059 - loss: 1.8983 - val_accuracy: 0.1964 - val_loss: 2.1030 - learning_rate: 0.0010
Epoch 14/50
14/14 ━━━━━━━━━━━━━━━━━━━━ 0s 186ms/step - accuracy: 0.3163 - loss: 1.9678
Epoch 14: val_loss improved from 1.98847 to 1.94321, saving model to cnn_model_2.weights.h5
14/14 ━━━━━━━━━━━━━━━━━━━━ 4s 209ms/step - accuracy: 0.3154 - loss: 1.9674 - val_accuracy: 0.2857 - val_loss: 1.9432 - learning_rate: 0.0010
Epoch 15/50
14/14 ━━━━━━━━━━━━━━━━━━━━ 0s 178ms/step - accuracy: 0.3153 - loss: 1.8916
Epoch 15: val_loss did not improve from 1.94321
14/14 ━━━━━━━━━━━━━━━━━━━━ 3s 191ms/step - accuracy: 0.3153 - loss: 1.8913 - val_accuracy: 0.2857 - val_loss: 1.9540 - learning_rate: 0.0010
Epoch 16/50
14/14 ━━━━━━━━━━━━━━━━━━━━ 0s 181ms/step - accuracy: 0.3577 - loss: 1.8141
Epoch 16: val_loss did not improve from 1.94321
14/14 ━━━━━━━━━━━━━━━━━━━━ 3s 196ms/step - accuracy: 0.3555 - loss: 1.8179 - val_accuracy: 0.3214 - val_loss: 1.9634 - learning_rate: 0.0010
Epoch 17/50
14/14 ━━━━━━━━━━━━━━━━━━━━ 0s 265ms/step - accuracy: 0.3951 - loss: 1.8192
Epoch 17: val_loss did not improve from 1.94321
14/14 ━━━━━━━━━━━━━━━━━━━━ 4s 290ms/step - accuracy: 0.3937 - loss: 1.8192 - val_accuracy: 0.2857 - val_loss: 2.0764 - learning_rate: 0.0010
Epoch 18/50
14/14 ━━━━━━━━━━━━━━━━━━━━ 0s 186ms/step - accuracy: 0.3680 - loss: 1.8687
Epoch 18: val_loss did not improve from 1.94321
14/14 ━━━━━━━━━━━━━━━━━━━━ 4s 199ms/step - accuracy: 0.3675 - loss: 1.8666 - val_accuracy: 0.3036 - val_loss: 2.0689 - learning_rate: 0.0010
Epoch 19/50
14/14 ━━━━━━━━━━━━━━━━━━━━ 0s 186ms/step - accuracy: 0.3942 - loss: 1.7806
Epoch 19: val_loss did not improve from 1.94321
14/14 ━━━━━━━━━━━━━━━━━━━━ 5s 200ms/step - accuracy: 0.3942 - loss: 1.7803 - val_accuracy: 0.3750 - val_loss: 1.9793 - learning_rate: 0.0010
Epoch 20/50
14/14 ━━━━━━━━━━━━━━━━━━━━ 0s 250ms/step - accuracy: 0.3576 - loss: 1.7875
Epoch 20: val_loss improved from 1.94321 to 1.91844, saving model to cnn_model_2.weights.h5
14/14 ━━━━━━━━━━━━━━━━━━━━ 4s 289ms/step - accuracy: 0.3590 - loss: 1.7869 - val_accuracy: 0.4286 - val_loss: 1.9184 - learning_rate: 0.0010
Epoch 21/50
14/14 ━━━━━━━━━━━━━━━━━━━━ 0s 188ms/step - accuracy: 0.3200 - loss: 1.8562
Epoch 21: val_loss did not improve from 1.91844
14/14 ━━━━━━━━━━━━━━━━━━━━ 4s 202ms/step - accuracy: 0.3219 - loss: 1.8527 - val_accuracy: 0.2857 - val_loss: 1.9747 - learning_rate: 0.0010
Epoch 22/50
14/14 ━━━━━━━━━━━━━━━━━━━━ 0s 186ms/step - accuracy: 0.3887 - loss: 1.7691
Epoch 22: val_loss did not improve from 1.91844
14/14 ━━━━━━━━━━━━━━━━━━━━ 3s 199ms/step - accuracy: 0.3890 - loss: 1.7693 - val_accuracy: 0.3571 - val_loss: 1.9332 - learning_rate: 0.0010
Epoch 23/50
14/14 ━━━━━━━━━━━━━━━━━━━━ 0s 183ms/step - accuracy: 0.3879 - loss: 1.7563
Epoch 23: val_loss did not improve from 1.91844
14/14 ━━━━━━━━━━━━━━━━━━━━ 3s 196ms/step - accuracy: 0.3876 - loss: 1.7544 - val_accuracy: 0.3393 - val_loss: 2.0281 - learning_rate: 0.0010
Epoch 24/50
14/14 ━━━━━━━━━━━━━━━━━━━━ 0s 271ms/step - accuracy: 0.4219 - loss: 1.6812
Epoch 24: val_loss improved from 1.91844 to 1.86413, saving model to cnn_model_2.weights.h5
14/14 ━━━━━━━━━━━━━━━━━━━━ 4s 305ms/step - accuracy: 0.4210 - loss: 1.6805 - val_accuracy: 0.3393 - val_loss: 1.8641 - learning_rate: 0.0010
Epoch 25/50
14/14 ━━━━━━━━━━━━━━━━━━━━ 0s 188ms/step - accuracy: 0.4650 - loss: 1.5726
Epoch 25: val_loss improved from 1.86413 to 1.78121, saving model to cnn_model_2.weights.h5
14/14 ━━━━━━━━━━━━━━━━━━━━ 4s 212ms/step - accuracy: 0.4637 - loss: 1.5757 - val_accuracy: 0.4286 - val_loss: 1.7812 - learning_rate: 0.0010
Epoch 26/50
14/14 ━━━━━━━━━━━━━━━━━━━━ 0s 187ms/step - accuracy: 0.4326 - loss: 1.6128
Epoch 26: val_loss did not improve from 1.78121
14/14 ━━━━━━━━━━━━━━━━━━━━ 3s 212ms/step - accuracy: 0.4308 - loss: 1.6148 - val_accuracy: 0.3036 - val_loss: 1.8514 - learning_rate: 0.0010
Epoch 27/50
14/14 ━━━━━━━━━━━━━━━━━━━━ 0s 185ms/step - accuracy: 0.4305 - loss: 1.6536
Epoch 27: val_loss did not improve from 1.78121
14/14 ━━━━━━━━━━━━━━━━━━━━ 3s 199ms/step - accuracy: 0.4294 - loss: 1.6554 - val_accuracy: 0.3393 - val_loss: 1.9455 - learning_rate: 0.0010
Epoch 28/50
14/14 ━━━━━━━━━━━━━━━━━━━━ 0s 290ms/step - accuracy: 0.4085 - loss: 1.6231
Epoch 28: val_loss did not improve from 1.78121
14/14 ━━━━━━━━━━━━━━━━━━━━ 4s 315ms/step - accuracy: 0.4080 - loss: 1.6241 - val_accuracy: 0.4107 - val_loss: 1.9274 - learning_rate: 0.0010
Epoch 29/50
14/14 ━━━━━━━━━━━━━━━━━━━━ 0s 186ms/step - accuracy: 0.4514 - loss: 1.6446
Epoch 29: val_loss did not improve from 1.78121
14/14 ━━━━━━━━━━━━━━━━━━━━ 3s 198ms/step - accuracy: 0.4500 - loss: 1.6437 - val_accuracy: 0.3929 - val_loss: 1.8457 - learning_rate: 0.0010
Epoch 30/50
14/14 ━━━━━━━━━━━━━━━━━━━━ 0s 183ms/step - accuracy: 0.4422 - loss: 1.5868
Epoch 30: val_loss did not improve from 1.78121
14/14 ━━━━━━━━━━━━━━━━━━━━ 3s 197ms/step - accuracy: 0.4417 - loss: 1.5883 - val_accuracy: 0.3393 - val_loss: 1.9285 - learning_rate: 0.0010
Epoch 31/50
14/14 ━━━━━━━━━━━━━━━━━━━━ 0s 184ms/step - accuracy: 0.5040 - loss: 1.4677
Epoch 31: val_loss did not improve from 1.78121
14/14 ━━━━━━━━━━━━━━━━━━━━ 3s 209ms/step - accuracy: 0.5028 - loss: 1.4695 - val_accuracy: 0.2857 - val_loss: 2.1038 - learning_rate: 0.0010
Epoch 32/50
14/14 ━━━━━━━━━━━━━━━━━━━━ 0s 258ms/step - accuracy: 0.4455 - loss: 1.5679
Epoch 32: val_loss did not improve from 1.78121
14/14 ━━━━━━━━━━━━━━━━━━━━ 4s 283ms/step - accuracy: 0.4455 - loss: 1.5693 - val_accuracy: 0.3214 - val_loss: 1.8913 - learning_rate: 0.0010
Epoch 33/50
14/14 ━━━━━━━━━━━━━━━━━━━━ 0s 181ms/step - accuracy: 0.5022 - loss: 1.5076
Epoch 33: val_loss did not improve from 1.78121
14/14 ━━━━━━━━━━━━━━━━━━━━ 4s 194ms/step - accuracy: 0.5003 - loss: 1.5099 - val_accuracy: 0.3750 - val_loss: 1.8602 - learning_rate: 0.0010
Epoch 34/50
14/14 ━━━━━━━━━━━━━━━━━━━━ 0s 185ms/step - accuracy: 0.4211 - loss: 1.6381
Epoch 34: val_loss did not improve from 1.78121
14/14 ━━━━━━━━━━━━━━━━━━━━ 3s 198ms/step - accuracy: 0.4225 - loss: 1.6330 - val_accuracy: 0.3214 - val_loss: 1.8660 - learning_rate: 0.0010
Epoch 35/50
14/14 ━━━━━━━━━━━━━━━━━━━━ 0s 186ms/step - accuracy: 0.4825 - loss: 1.5003
Epoch 35: val_loss did not improve from 1.78121
14/14 ━━━━━━━━━━━━━━━━━━━━ 3s 201ms/step - accuracy: 0.4832 - loss: 1.4985 - val_accuracy: 0.3750 - val_loss: 1.9433 - learning_rate: 0.0010
Epoch 36/50
14/14 ━━━━━━━━━━━━━━━━━━━━ 0s 279ms/step - accuracy: 0.5250 - loss: 1.4188
Epoch 36: val_loss improved from 1.78121 to 1.75832, saving model to cnn_model_2.weights.h5
14/14 ━━━━━━━━━━━━━━━━━━━━ 4s 315ms/step - accuracy: 0.5235 - loss: 1.4210 - val_accuracy: 0.4464 - val_loss: 1.7583 - learning_rate: 5.0000e-04
Epoch 37/50
14/14 ━━━━━━━━━━━━━━━━━━━━ 0s 181ms/step - accuracy: 0.4737 - loss: 1.5109
Epoch 37: val_loss did not improve from 1.75832
14/14 ━━━━━━━━━━━━━━━━━━━━ 3s 195ms/step - accuracy: 0.4747 - loss: 1.5069 - val_accuracy: 0.4286 - val_loss: 1.7786 - learning_rate: 5.0000e-04
Epoch 38/50
14/14 ━━━━━━━━━━━━━━━━━━━━ 0s 183ms/step - accuracy: 0.4998 - loss: 1.3879
Epoch 38: val_loss did not improve from 1.75832
14/14 ━━━━━━━━━━━━━━━━━━━━ 5s 196ms/step - accuracy: 0.4995 - loss: 1.3885 - val_accuracy: 0.4821 - val_loss: 1.7657 - learning_rate: 5.0000e-04
Epoch 39/50
14/14 ━━━━━━━━━━━━━━━━━━━━ 0s 203ms/step - accuracy: 0.5556 - loss: 1.3541
Epoch 39: val_loss did not improve from 1.75832
14/14 ━━━━━━━━━━━━━━━━━━━━ 3s 222ms/step - accuracy: 0.5546 - loss: 1.3556 - val_accuracy: 0.3571 - val_loss: 1.7976 - learning_rate: 5.0000e-04
Epoch 40/50
14/14 ━━━━━━━━━━━━━━━━━━━━ 0s 186ms/step - accuracy: 0.4894 - loss: 1.3317
Epoch 40: val_loss did not improve from 1.75832
14/14 ━━━━━━━━━━━━━━━━━━━━ 5s 199ms/step - accuracy: 0.4901 - loss: 1.3336 - val_accuracy: 0.3929 - val_loss: 1.9269 - learning_rate: 5.0000e-04
Epoch 41/50
14/14 ━━━━━━━━━━━━━━━━━━━━ 0s 183ms/step - accuracy: 0.4893 - loss: 1.3505
Epoch 41: val_loss did not improve from 1.75832
14/14 ━━━━━━━━━━━━━━━━━━━━ 3s 197ms/step - accuracy: 0.4893 - loss: 1.3527 - val_accuracy: 0.4821 - val_loss: 1.8018 - learning_rate: 5.0000e-04
Epoch 42/50
14/14 ━━━━━━━━━━━━━━━━━━━━ 0s 189ms/step - accuracy: 0.5180 - loss: 1.2948
Epoch 42: val_loss improved from 1.75832 to 1.71094, saving model to cnn_model_2.weights.h5
14/14 ━━━━━━━━━━━━━━━━━━━━ 3s 212ms/step - accuracy: 0.5181 - loss: 1.2961 - val_accuracy: 0.4286 - val_loss: 1.7109 - learning_rate: 5.0000e-04
Epoch 43/50
14/14 ━━━━━━━━━━━━━━━━━━━━ 0s 218ms/step - accuracy: 0.5272 - loss: 1.3044
Epoch 43: val_loss did not improve from 1.71094
14/14 ━━━━━━━━━━━━━━━━━━━━ 5s 231ms/step - accuracy: 0.5264 - loss: 1.3064 - val_accuracy: 0.3750 - val_loss: 1.8483 - learning_rate: 5.0000e-04
Epoch 44/50
14/14 ━━━━━━━━━━━━━━━━━━━━ 0s 190ms/step - accuracy: 0.5827 - loss: 1.2260
Epoch 44: val_loss did not improve from 1.71094
14/14 ━━━━━━━━━━━━━━━━━━━━ 3s 204ms/step - accuracy: 0.5794 - loss: 1.2326 - val_accuracy: 0.4107 - val_loss: 1.7893 - learning_rate: 5.0000e-04
Epoch 45/50
14/14 ━━━━━━━━━━━━━━━━━━━━ 0s 184ms/step - accuracy: 0.5075 - loss: 1.3605
Epoch 45: val_loss did not improve from 1.71094
14/14 ━━━━━━━━━━━━━━━━━━━━ 3s 197ms/step - accuracy: 0.5098 - loss: 1.3555 - val_accuracy: 0.4464 - val_loss: 1.7259 - learning_rate: 5.0000e-04
Epoch 46/50
14/14 ━━━━━━━━━━━━━━━━━━━━ 0s 196ms/step - accuracy: 0.5507 - loss: 1.2950
Epoch 46: val_loss did not improve from 1.71094
14/14 ━━━━━━━━━━━━━━━━━━━━ 3s 214ms/step - accuracy: 0.5499 - loss: 1.2937 - val_accuracy: 0.4821 - val_loss: 1.9161 - learning_rate: 5.0000e-04
Epoch 47/50
14/14 ━━━━━━━━━━━━━━━━━━━━ 0s 260ms/step - accuracy: 0.5112 - loss: 1.3212
Epoch 47: val_loss did not improve from 1.71094
14/14 ━━━━━━━━━━━━━━━━━━━━ 4s 272ms/step - accuracy: 0.5132 - loss: 1.3201 - val_accuracy: 0.3571 - val_loss: 2.0501 - learning_rate: 5.0000e-04
Epoch 48/50
14/14 ━━━━━━━━━━━━━━━━━━━━ 0s 192ms/step - accuracy: 0.5673 - loss: 1.3060
Epoch 48: val_loss did not improve from 1.71094
14/14 ━━━━━━━━━━━━━━━━━━━━ 3s 206ms/step - accuracy: 0.5682 - loss: 1.3028 - val_accuracy: 0.3929 - val_loss: 1.8141 - learning_rate: 5.0000e-04
Epoch 49/50
14/14 ━━━━━━━━━━━━━━━━━━━━ 0s 183ms/step - accuracy: 0.5567 - loss: 1.2186
Epoch 49: val_loss did not improve from 1.71094
14/14 ━━━━━━━━━━━━━━━━━━━━ 3s 196ms/step - accuracy: 0.5567 - loss: 1.2203 - val_accuracy: 0.4464 - val_loss: 1.9469 - learning_rate: 5.0000e-04
Epoch 50/50
14/14 ━━━━━━━━━━━━━━━━━━━━ 0s 186ms/step - accuracy: 0.5881 - loss: 1.2170
Epoch 50: val_loss did not improve from 1.71094
14/14 ━━━━━━━━━━━━━━━━━━━━ 3s 199ms/step - accuracy: 0.5867 - loss: 1.2198 - val_accuracy: 0.3929 - val_loss: 1.9444 - learning_rate: 5.0000e-04
InΒ [Β ]:
plot_model_accuracy(history,cnn_model_2)
No description has been provided for this image
InΒ [Β ]:
cnn_model_2.evaluate(test_gen)

2/2 ━━━━━━━━━━━━━━━━━━━━ 0s 70ms/step - accuracy: 0.4643 - loss: 1.6471
Out[Β ]:
[1.7094813585281372, 0.4464285671710968]
InΒ [Β ]:
# SHow classfication report
generate_classification_report(cnn_model_2, test_gen)

WARNING:tensorflow:5 out of the last 9 calls to <function TensorFlowTrainer.make_predict_function.<locals>.one_step_on_data_distributed at 0x7b41e1c942c0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.

1/2 ━━━━━━━━━━━━━━━━━━━━ 0s 586ms/step
WARNING:tensorflow:6 out of the last 10 calls to <function TensorFlowTrainer.make_predict_function.<locals>.one_step_on_data_distributed at 0x7b41e1c942c0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.

2/2 ━━━━━━━━━━━━━━━━━━━━ 1s 366ms/step
                      precision    recall  f1-score   support

      chocolate_cake       0.80      0.80      0.80         5
        french_fries       0.40      0.50      0.44         8
           ice_cream       0.43      0.50      0.46         6
              nachos       0.00      0.00      0.00         5
          onion_ring       0.25      0.20      0.22         5
               pizza       0.55      1.00      0.71         6
              samosa       0.40      0.29      0.33         7
strawberry_shortcake       1.00      0.60      0.75         5
               tacos       0.40      0.40      0.40         5
              waffle       0.00      0.00      0.00         4

            accuracy                           0.45        56
           macro avg       0.42      0.43      0.41        56
        weighted avg       0.43      0.45      0.42        56
InΒ [Β ]:
plot_confusion_matrix(cnn_model_2,test_gen,list(test_gen.class_indices.keys()))

2/2 ━━━━━━━━━━━━━━━━━━━━ 0s 61ms/step
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ObservationΒΆ

  • Test accuracy is at 45%
  • Overall Accuracy: 43% β€” lesser from previous model which is at 50% .
  • Well-Classified Classes:
    • chocolate_cake, pizza, ice_cream: Good precision and recall.
  • Moderate Performance:
    • french_fries, tacos, onion_ring: Acceptable but need improvement.
  • Poorly Classified Classes:
    • samosa, nachos, waffle, strawberry_shortcake: Low recall or F1-score; waffle has 0% recall.

With he_normal and SGDΒΆ

InΒ [Β ]:
checkpointer, earlystopping, reduceLR=get_callbacks(filepath='cnn_model_sgd.weights.h5')
InΒ [Β ]:
K.clear_session()
cnn_model_sgd=build_basic_cnn_2((128, 128, 3), 10,SGD(learning_rate=0.001))

/usr/local/lib/python3.11/dist-packages/keras/src/layers/convolutional/base_conv.py:107: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.
  super().__init__(activity_regularizer=activity_regularizer, **kwargs)
InΒ [Β ]:
cnn_model_sgd.compile(
    optimizer='adam',
    loss='categorical_crossentropy',
    metrics=['accuracy']
)
InΒ [Β ]:
history = cnn_model_sgd.fit(
    train_gen,
    epochs=50,
    validation_data=valid_gen,
    callbacks=[checkpointer, reduceLR, earlystopping]
    )

Epoch 1/50
14/14 ━━━━━━━━━━━━━━━━━━━━ 0s 281ms/step - accuracy: 0.1100 - loss: 2.9104
Epoch 1: val_loss improved from inf to 2.28927, saving model to cnn_model_sgd.weights.h5
14/14 ━━━━━━━━━━━━━━━━━━━━ 11s 425ms/step - accuracy: 0.1116 - loss: 2.8890 - val_accuracy: 0.1071 - val_loss: 2.2893 - learning_rate: 0.0010
Epoch 2/50
14/14 ━━━━━━━━━━━━━━━━━━━━ 0s 177ms/step - accuracy: 0.1326 - loss: 2.3050
Epoch 2: val_loss improved from 2.28927 to 2.27802, saving model to cnn_model_sgd.weights.h5
14/14 ━━━━━━━━━━━━━━━━━━━━ 3s 199ms/step - accuracy: 0.1324 - loss: 2.3047 - val_accuracy: 0.1429 - val_loss: 2.2780 - learning_rate: 0.0010
Epoch 3/50
14/14 ━━━━━━━━━━━━━━━━━━━━ 0s 181ms/step - accuracy: 0.1410 - loss: 2.2769
Epoch 3: val_loss improved from 2.27802 to 2.25607, saving model to cnn_model_sgd.weights.h5
14/14 ━━━━━━━━━━━━━━━━━━━━ 3s 204ms/step - accuracy: 0.1400 - loss: 2.2771 - val_accuracy: 0.2143 - val_loss: 2.2561 - learning_rate: 0.0010
Epoch 4/50
14/14 ━━━━━━━━━━━━━━━━━━━━ 0s 184ms/step - accuracy: 0.2038 - loss: 2.2205
Epoch 4: val_loss improved from 2.25607 to 2.23521, saving model to cnn_model_sgd.weights.h5
14/14 ━━━━━━━━━━━━━━━━━━━━ 3s 209ms/step - accuracy: 0.2029 - loss: 2.2209 - val_accuracy: 0.2143 - val_loss: 2.2352 - learning_rate: 0.0010
Epoch 5/50
14/14 ━━━━━━━━━━━━━━━━━━━━ 0s 303ms/step - accuracy: 0.1892 - loss: 2.1896
Epoch 5: val_loss improved from 2.23521 to 2.22589, saving model to cnn_model_sgd.weights.h5
14/14 ━━━━━━━━━━━━━━━━━━━━ 5s 345ms/step - accuracy: 0.1885 - loss: 2.1909 - val_accuracy: 0.2321 - val_loss: 2.2259 - learning_rate: 0.0010
Epoch 6/50
14/14 ━━━━━━━━━━━━━━━━━━━━ 0s 204ms/step - accuracy: 0.1889 - loss: 2.1512
Epoch 6: val_loss improved from 2.22589 to 2.16563, saving model to cnn_model_sgd.weights.h5
14/14 ━━━━━━━━━━━━━━━━━━━━ 4s 227ms/step - accuracy: 0.1898 - loss: 2.1511 - val_accuracy: 0.3036 - val_loss: 2.1656 - learning_rate: 0.0010
Epoch 7/50
14/14 ━━━━━━━━━━━━━━━━━━━━ 0s 183ms/step - accuracy: 0.2242 - loss: 2.1898
Epoch 7: val_loss did not improve from 2.16563
14/14 ━━━━━━━━━━━━━━━━━━━━ 3s 208ms/step - accuracy: 0.2231 - loss: 2.1909 - val_accuracy: 0.1607 - val_loss: 2.2050 - learning_rate: 0.0010
Epoch 8/50
14/14 ━━━━━━━━━━━━━━━━━━━━ 0s 184ms/step - accuracy: 0.2006 - loss: 2.1750
Epoch 8: val_loss improved from 2.16563 to 2.14811, saving model to cnn_model_sgd.weights.h5
14/14 ━━━━━━━━━━━━━━━━━━━━ 3s 207ms/step - accuracy: 0.2020 - loss: 2.1719 - val_accuracy: 0.2500 - val_loss: 2.1481 - learning_rate: 0.0010
Epoch 9/50
14/14 ━━━━━━━━━━━━━━━━━━━━ 0s 206ms/step - accuracy: 0.2559 - loss: 2.1249
Epoch 9: val_loss improved from 2.14811 to 2.12755, saving model to cnn_model_sgd.weights.h5
14/14 ━━━━━━━━━━━━━━━━━━━━ 3s 247ms/step - accuracy: 0.2546 - loss: 2.1248 - val_accuracy: 0.2321 - val_loss: 2.1276 - learning_rate: 0.0010
Epoch 10/50
14/14 ━━━━━━━━━━━━━━━━━━━━ 0s 223ms/step - accuracy: 0.2645 - loss: 2.0155
Epoch 10: val_loss improved from 2.12755 to 2.04542, saving model to cnn_model_sgd.weights.h5
14/14 ━━━━━━━━━━━━━━━━━━━━ 3s 243ms/step - accuracy: 0.2639 - loss: 2.0182 - val_accuracy: 0.2857 - val_loss: 2.0454 - learning_rate: 0.0010
Epoch 11/50
14/14 ━━━━━━━━━━━━━━━━━━━━ 0s 176ms/step - accuracy: 0.2584 - loss: 2.0184
Epoch 11: val_loss did not improve from 2.04542
14/14 ━━━━━━━━━━━━━━━━━━━━ 3s 189ms/step - accuracy: 0.2580 - loss: 2.0176 - val_accuracy: 0.2321 - val_loss: 2.0517 - learning_rate: 0.0010
Epoch 12/50
14/14 ━━━━━━━━━━━━━━━━━━━━ 0s 178ms/step - accuracy: 0.2994 - loss: 1.9987
Epoch 12: val_loss did not improve from 2.04542
14/14 ━━━━━━━━━━━━━━━━━━━━ 3s 191ms/step - accuracy: 0.2998 - loss: 1.9971 - val_accuracy: 0.2321 - val_loss: 2.1428 - learning_rate: 0.0010
Epoch 13/50
14/14 ━━━━━━━━━━━━━━━━━━━━ 0s 181ms/step - accuracy: 0.2821 - loss: 2.0251
Epoch 13: val_loss did not improve from 2.04542
14/14 ━━━━━━━━━━━━━━━━━━━━ 3s 194ms/step - accuracy: 0.2815 - loss: 2.0239 - val_accuracy: 0.1964 - val_loss: 2.0622 - learning_rate: 0.0010
Epoch 14/50
14/14 ━━━━━━━━━━━━━━━━━━━━ 0s 275ms/step - accuracy: 0.3319 - loss: 1.9566
Epoch 14: val_loss improved from 2.04542 to 1.98582, saving model to cnn_model_sgd.weights.h5
14/14 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 0.3306 - loss: 1.9562 - val_accuracy: 0.3036 - val_loss: 1.9858 - learning_rate: 0.0010
Epoch 15/50
14/14 ━━━━━━━━━━━━━━━━━━━━ 0s 181ms/step - accuracy: 0.3014 - loss: 1.9413
Epoch 15: val_loss improved from 1.98582 to 1.97371, saving model to cnn_model_sgd.weights.h5
14/14 ━━━━━━━━━━━━━━━━━━━━ 3s 217ms/step - accuracy: 0.3008 - loss: 1.9419 - val_accuracy: 0.3036 - val_loss: 1.9737 - learning_rate: 0.0010
Epoch 16/50
14/14 ━━━━━━━━━━━━━━━━━━━━ 0s 185ms/step - accuracy: 0.3357 - loss: 1.9420
Epoch 16: val_loss did not improve from 1.97371
14/14 ━━━━━━━━━━━━━━━━━━━━ 3s 197ms/step - accuracy: 0.3348 - loss: 1.9394 - val_accuracy: 0.3036 - val_loss: 1.9760 - learning_rate: 0.0010
Epoch 17/50
14/14 ━━━━━━━━━━━━━━━━━━━━ 0s 182ms/step - accuracy: 0.3450 - loss: 1.7980
Epoch 17: val_loss improved from 1.97371 to 1.88588, saving model to cnn_model_sgd.weights.h5
14/14 ━━━━━━━━━━━━━━━━━━━━ 3s 205ms/step - accuracy: 0.3449 - loss: 1.8012 - val_accuracy: 0.3929 - val_loss: 1.8859 - learning_rate: 0.0010
Epoch 18/50
14/14 ━━━━━━━━━━━━━━━━━━━━ 0s 257ms/step - accuracy: 0.3129 - loss: 1.8551
Epoch 18: val_loss did not improve from 1.88588
14/14 ━━━━━━━━━━━━━━━━━━━━ 4s 282ms/step - accuracy: 0.3146 - loss: 1.8574 - val_accuracy: 0.2321 - val_loss: 1.9886 - learning_rate: 0.0010
Epoch 19/50
14/14 ━━━━━━━━━━━━━━━━━━━━ 0s 183ms/step - accuracy: 0.4453 - loss: 1.7280
Epoch 19: val_loss did not improve from 1.88588
14/14 ━━━━━━━━━━━━━━━━━━━━ 4s 196ms/step - accuracy: 0.4420 - loss: 1.7295 - val_accuracy: 0.2679 - val_loss: 2.0515 - learning_rate: 0.0010
Epoch 20/50
14/14 ━━━━━━━━━━━━━━━━━━━━ 0s 179ms/step - accuracy: 0.3411 - loss: 1.8667
Epoch 20: val_loss improved from 1.88588 to 1.87394, saving model to cnn_model_sgd.weights.h5
14/14 ━━━━━━━━━━━━━━━━━━━━ 3s 202ms/step - accuracy: 0.3412 - loss: 1.8670 - val_accuracy: 0.3393 - val_loss: 1.8739 - learning_rate: 0.0010
Epoch 21/50
14/14 ━━━━━━━━━━━━━━━━━━━━ 0s 240ms/step - accuracy: 0.3288 - loss: 1.7780
Epoch 21: val_loss did not improve from 1.87394
14/14 ━━━━━━━━━━━━━━━━━━━━ 6s 257ms/step - accuracy: 0.3296 - loss: 1.7795 - val_accuracy: 0.2500 - val_loss: 2.1499 - learning_rate: 0.0010
Epoch 22/50
14/14 ━━━━━━━━━━━━━━━━━━━━ 0s 183ms/step - accuracy: 0.3726 - loss: 1.7188
Epoch 22: val_loss did not improve from 1.87394
14/14 ━━━━━━━━━━━━━━━━━━━━ 4s 197ms/step - accuracy: 0.3727 - loss: 1.7211 - val_accuracy: 0.3036 - val_loss: 2.3458 - learning_rate: 0.0010
Epoch 23/50
14/14 ━━━━━━━━━━━━━━━━━━━━ 0s 180ms/step - accuracy: 0.3565 - loss: 1.7969
Epoch 23: val_loss improved from 1.87394 to 1.87252, saving model to cnn_model_sgd.weights.h5
14/14 ━━━━━━━━━━━━━━━━━━━━ 3s 203ms/step - accuracy: 0.3563 - loss: 1.7967 - val_accuracy: 0.3929 - val_loss: 1.8725 - learning_rate: 0.0010
Epoch 24/50
14/14 ━━━━━━━━━━━━━━━━━━━━ 0s 187ms/step - accuracy: 0.3932 - loss: 1.7071
Epoch 24: val_loss did not improve from 1.87252
14/14 ━━━━━━━━━━━━━━━━━━━━ 3s 200ms/step - accuracy: 0.3939 - loss: 1.7046 - val_accuracy: 0.3214 - val_loss: 2.0628 - learning_rate: 0.0010
Epoch 25/50
14/14 ━━━━━━━━━━━━━━━━━━━━ 0s 271ms/step - accuracy: 0.4065 - loss: 1.6843
Epoch 25: val_loss did not improve from 1.87252
14/14 ━━━━━━━━━━━━━━━━━━━━ 4s 290ms/step - accuracy: 0.4044 - loss: 1.6871 - val_accuracy: 0.3750 - val_loss: 1.9016 - learning_rate: 0.0010
Epoch 26/50
14/14 ━━━━━━━━━━━━━━━━━━━━ 0s 179ms/step - accuracy: 0.3919 - loss: 1.7254
Epoch 26: val_loss did not improve from 1.87252
14/14 ━━━━━━━━━━━━━━━━━━━━ 3s 193ms/step - accuracy: 0.3919 - loss: 1.7255 - val_accuracy: 0.2321 - val_loss: 2.0038 - learning_rate: 0.0010
Epoch 27/50
14/14 ━━━━━━━━━━━━━━━━━━━━ 0s 181ms/step - accuracy: 0.4522 - loss: 1.6339
Epoch 27: val_loss did not improve from 1.87252
14/14 ━━━━━━━━━━━━━━━━━━━━ 3s 194ms/step - accuracy: 0.4500 - loss: 1.6347 - val_accuracy: 0.3214 - val_loss: 1.9812 - learning_rate: 0.0010
Epoch 28/50
14/14 ━━━━━━━━━━━━━━━━━━━━ 0s 180ms/step - accuracy: 0.3705 - loss: 1.6781
Epoch 28: val_loss did not improve from 1.87252
14/14 ━━━━━━━━━━━━━━━━━━━━ 3s 193ms/step - accuracy: 0.3720 - loss: 1.6766 - val_accuracy: 0.2857 - val_loss: 2.1018 - learning_rate: 0.0010
Epoch 29/50
14/14 ━━━━━━━━━━━━━━━━━━━━ 0s 212ms/step - accuracy: 0.4646 - loss: 1.5609
Epoch 29: val_loss did not improve from 1.87252
14/14 ━━━━━━━━━━━━━━━━━━━━ 3s 233ms/step - accuracy: 0.4653 - loss: 1.5616 - val_accuracy: 0.3036 - val_loss: 2.1454 - learning_rate: 0.0010
Epoch 30/50
14/14 ━━━━━━━━━━━━━━━━━━━━ 0s 226ms/step - accuracy: 0.4553 - loss: 1.5652
Epoch 30: val_loss did not improve from 1.87252
14/14 ━━━━━━━━━━━━━━━━━━━━ 3s 239ms/step - accuracy: 0.4531 - loss: 1.5665 - val_accuracy: 0.3929 - val_loss: 2.0364 - learning_rate: 0.0010
Epoch 31/50
14/14 ━━━━━━━━━━━━━━━━━━━━ 0s 180ms/step - accuracy: 0.3819 - loss: 1.6918
Epoch 31: val_loss did not improve from 1.87252
14/14 ━━━━━━━━━━━━━━━━━━━━ 3s 194ms/step - accuracy: 0.3829 - loss: 1.6930 - val_accuracy: 0.2679 - val_loss: 1.9998 - learning_rate: 0.0010
Epoch 32/50
14/14 ━━━━━━━━━━━━━━━━━━━━ 0s 193ms/step - accuracy: 0.4224 - loss: 1.6644
Epoch 32: val_loss did not improve from 1.87252
14/14 ━━━━━━━━━━━━━━━━━━━━ 3s 205ms/step - accuracy: 0.4214 - loss: 1.6657 - val_accuracy: 0.3571 - val_loss: 1.8833 - learning_rate: 0.0010
Epoch 33/50
14/14 ━━━━━━━━━━━━━━━━━━━━ 0s 178ms/step - accuracy: 0.4551 - loss: 1.5783
Epoch 33: val_loss did not improve from 1.87252
14/14 ━━━━━━━━━━━━━━━━━━━━ 3s 194ms/step - accuracy: 0.4549 - loss: 1.5794 - val_accuracy: 0.3750 - val_loss: 1.9177 - learning_rate: 0.0010
Epoch 34/50
14/14 ━━━━━━━━━━━━━━━━━━━━ 0s 266ms/step - accuracy: 0.5345 - loss: 1.4077
Epoch 34: val_loss did not improve from 1.87252
14/14 ━━━━━━━━━━━━━━━━━━━━ 4s 280ms/step - accuracy: 0.5311 - loss: 1.4153 - val_accuracy: 0.3393 - val_loss: 1.9116 - learning_rate: 5.0000e-04
Epoch 35/50
14/14 ━━━━━━━━━━━━━━━━━━━━ 0s 183ms/step - accuracy: 0.4230 - loss: 1.5021
Epoch 35: val_loss did not improve from 1.87252
14/14 ━━━━━━━━━━━━━━━━━━━━ 3s 197ms/step - accuracy: 0.4247 - loss: 1.5024 - val_accuracy: 0.3393 - val_loss: 2.0294 - learning_rate: 5.0000e-04
Epoch 36/50
14/14 ━━━━━━━━━━━━━━━━━━━━ 0s 177ms/step - accuracy: 0.5043 - loss: 1.4778
Epoch 36: val_loss did not improve from 1.87252
14/14 ━━━━━━━━━━━━━━━━━━━━ 3s 190ms/step - accuracy: 0.5029 - loss: 1.4772 - val_accuracy: 0.3214 - val_loss: 2.0046 - learning_rate: 5.0000e-04
Epoch 37/50
14/14 ━━━━━━━━━━━━━━━━━━━━ 0s 186ms/step - accuracy: 0.4781 - loss: 1.4969
Epoch 37: val_loss improved from 1.87252 to 1.84682, saving model to cnn_model_sgd.weights.h5
14/14 ━━━━━━━━━━━━━━━━━━━━ 3s 209ms/step - accuracy: 0.4785 - loss: 1.4962 - val_accuracy: 0.3929 - val_loss: 1.8468 - learning_rate: 5.0000e-04
Epoch 38/50
14/14 ━━━━━━━━━━━━━━━━━━━━ 0s 257ms/step - accuracy: 0.5263 - loss: 1.3728
Epoch 38: val_loss did not improve from 1.84682
14/14 ━━━━━━━━━━━━━━━━━━━━ 4s 282ms/step - accuracy: 0.5235 - loss: 1.3782 - val_accuracy: 0.4286 - val_loss: 1.9290 - learning_rate: 5.0000e-04
Epoch 39/50
14/14 ━━━━━━━━━━━━━━━━━━━━ 0s 217ms/step - accuracy: 0.5332 - loss: 1.3605
Epoch 39: val_loss did not improve from 1.84682
14/14 ━━━━━━━━━━━━━━━━━━━━ 4s 230ms/step - accuracy: 0.5314 - loss: 1.3675 - val_accuracy: 0.2857 - val_loss: 2.0903 - learning_rate: 5.0000e-04
Epoch 40/50
14/14 ━━━━━━━━━━━━━━━━━━━━ 0s 185ms/step - accuracy: 0.5014 - loss: 1.4910
Epoch 40: val_loss did not improve from 1.84682
14/14 ━━━━━━━━━━━━━━━━━━━━ 3s 199ms/step - accuracy: 0.5007 - loss: 1.4898 - val_accuracy: 0.2857 - val_loss: 2.1363 - learning_rate: 5.0000e-04
Epoch 41/50
14/14 ━━━━━━━━━━━━━━━━━━━━ 0s 182ms/step - accuracy: 0.5019 - loss: 1.3833
Epoch 41: val_loss did not improve from 1.84682
14/14 ━━━━━━━━━━━━━━━━━━━━ 3s 196ms/step - accuracy: 0.5014 - loss: 1.3843 - val_accuracy: 0.4107 - val_loss: 1.9062 - learning_rate: 5.0000e-04
Epoch 42/50
14/14 ━━━━━━━━━━━━━━━━━━━━ 0s 285ms/step - accuracy: 0.5022 - loss: 1.3820
Epoch 42: val_loss did not improve from 1.84682
14/14 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 0.5032 - loss: 1.3827 - val_accuracy: 0.3929 - val_loss: 1.9240 - learning_rate: 5.0000e-04
Epoch 43/50
14/14 ━━━━━━━━━━━━━━━━━━━━ 0s 184ms/step - accuracy: 0.4825 - loss: 1.3955
Epoch 43: val_loss did not improve from 1.84682
14/14 ━━━━━━━━━━━━━━━━━━━━ 3s 197ms/step - accuracy: 0.4837 - loss: 1.3966 - val_accuracy: 0.4107 - val_loss: 2.0472 - learning_rate: 5.0000e-04
Epoch 44/50
14/14 ━━━━━━━━━━━━━━━━━━━━ 0s 184ms/step - accuracy: 0.5388 - loss: 1.3734
Epoch 44: val_loss did not improve from 1.84682
14/14 ━━━━━━━━━━━━━━━━━━━━ 3s 196ms/step - accuracy: 0.5372 - loss: 1.3731 - val_accuracy: 0.3929 - val_loss: 1.9251 - learning_rate: 5.0000e-04
Epoch 45/50
14/14 ━━━━━━━━━━━━━━━━━━━━ 0s 183ms/step - accuracy: 0.5809 - loss: 1.2577
Epoch 45: val_loss did not improve from 1.84682
14/14 ━━━━━━━━━━━━━━━━━━━━ 3s 197ms/step - accuracy: 0.5776 - loss: 1.2643 - val_accuracy: 0.4464 - val_loss: 2.0313 - learning_rate: 5.0000e-04
Epoch 46/50
14/14 ━━━━━━━━━━━━━━━━━━━━ 0s 241ms/step - accuracy: 0.4971 - loss: 1.3705
Epoch 46: val_loss did not improve from 1.84682
14/14 ━━━━━━━━━━━━━━━━━━━━ 4s 267ms/step - accuracy: 0.4975 - loss: 1.3701 - val_accuracy: 0.3393 - val_loss: 2.0426 - learning_rate: 5.0000e-04
Epoch 47/50
14/14 ━━━━━━━━━━━━━━━━━━━━ 0s 182ms/step - accuracy: 0.5388 - loss: 1.3390
Epoch 47: val_loss did not improve from 1.84682
14/14 ━━━━━━━━━━━━━━━━━━━━ 4s 196ms/step - accuracy: 0.5392 - loss: 1.3381 - val_accuracy: 0.4286 - val_loss: 1.9620 - learning_rate: 5.0000e-04
Epoch 48/50
14/14 ━━━━━━━━━━━━━━━━━━━━ 0s 184ms/step - accuracy: 0.5252 - loss: 1.3582
Epoch 48: val_loss improved from 1.84682 to 1.80703, saving model to cnn_model_sgd.weights.h5
14/14 ━━━━━━━━━━━━━━━━━━━━ 3s 207ms/step - accuracy: 0.5261 - loss: 1.3550 - val_accuracy: 0.4464 - val_loss: 1.8070 - learning_rate: 2.5000e-04
Epoch 49/50
14/14 ━━━━━━━━━━━━━━━━━━━━ 0s 186ms/step - accuracy: 0.5866 - loss: 1.1867
Epoch 49: val_loss did not improve from 1.80703
14/14 ━━━━━━━━━━━━━━━━━━━━ 3s 199ms/step - accuracy: 0.5835 - loss: 1.1934 - val_accuracy: 0.3929 - val_loss: 2.0484 - learning_rate: 2.5000e-04
Epoch 50/50
14/14 ━━━━━━━━━━━━━━━━━━━━ 0s 267ms/step - accuracy: 0.5657 - loss: 1.2242
Epoch 50: val_loss did not improve from 1.80703
14/14 ━━━━━━━━━━━━━━━━━━━━ 4s 293ms/step - accuracy: 0.5634 - loss: 1.2289 - val_accuracy: 0.3750 - val_loss: 1.9490 - learning_rate: 2.5000e-04
InΒ [Β ]:
plot_model_accuracy(history,cnn_model_sgd)
No description has been provided for this image
InΒ [Β ]:
cnn_model_sgd.evaluate(test_gen)

2/2 ━━━━━━━━━━━━━━━━━━━━ 0s 67ms/step - accuracy: 0.5000 - loss: 1.6595
Out[Β ]:
[1.6906964778900146, 0.5]

Observations:ΒΆ

Not much Improvemnet with previous models .SGD did not make the curve smooth.Though test score is same as first model but the training and validation score are lesser then first model .

InΒ [Β ]:
# SHow classfication report
generate_classification_report(cnn_model_sgd, test_gen)

2/2 ━━━━━━━━━━━━━━━━━━━━ 1s 268ms/step
                      precision    recall  f1-score   support

      chocolate_cake       0.83      1.00      0.91         5
        french_fries       0.50      0.75      0.60         8
           ice_cream       0.60      0.50      0.55         6
              nachos       0.00      0.00      0.00         5
          onion_ring       0.50      0.40      0.44         5
               pizza       0.50      1.00      0.67         6
              samosa       0.25      0.14      0.18         7
strawberry_shortcake       1.00      0.60      0.75         5
               tacos       0.40      0.40      0.40         5
              waffle       0.00      0.00      0.00         4

            accuracy                           0.50        56
           macro avg       0.46      0.48      0.45        56
        weighted avg       0.46      0.50      0.46        56
InΒ [Β ]:
# Show confusion matrix
plot_confusion_matrix(cnn_model_sgd,test_gen,list(test_gen.class_indices.keys()))

2/2 ━━━━━━━━━━━━━━━━━━━━ 0s 71ms/step
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Observations:ΒΆ

  • Test Accuracy: 50%
  • Overall Accuracy: 46% less then 1st model (without he_normal)
  • High Performing Classes: chocolate_cake, pizza, ice_cream show good precision and recall.
  • Poorly Learned Classes: samosa, onion_ring, strawberry_shortcake β€” either completely misclassified or low recall.

Model performances is almost similar to 1st cnn model without he_normal and sgd(used Adam)

Performances(Trained with Balnaced train,test,validation set):ΒΆ

Class-wise ObservationsΒΆ

Class Best Model (based on F1) Comments
chocolate_cake Model 3(SGD+he_normal) (0.91) Excellent in all models.
french_fries Model 3(SGD+he_normal)) (0.60) Improved with SGD + He Normal.
ice_cream Model 1(Adam) (0.62) Slight drop in Model 3.
nachos Model 1(Adam) (0.25) Still poor in all.
onion_ring Model 3(SGD+he_normal) (0.44) He Normal helped.
pizza Tie (1.00 recall in all) Always predicted correctly.
samosa Model 2(Adam+henormal) (0.33) Still weak prediction.
strawberry_shortcake Model 1 (0.57) / Model 2 & 3 (0.75) Higher precision and recall with He Normal.
tacos Tie (~0.40 F1 across) Consistent poor performance.
waffle Model 1(Adam) (0.29) Missed completely in Model 2 & 3.

Visualizing the prediction (Model-1 Default Initializer and Adam):ΒΆ

InΒ [Β ]:
# Visualise some predictions
visualize_predictions(model_2,test_gen,list(test_gen.class_indices.keys()))

1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 282ms/step
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Observations:ΒΆ

  • Strongest classes: chocolate_cake, pizza, ice_cream,French Fries

  • Performs better on visually distinct items.

Weaknesses:

  • Fails on subtle or similar-looking items: samosa, waffle, nachos, and strawberry_shortcake.

  • Somecases if there is some mayo like item in image it predict it as ice_cream might be due to similarity with icecream and mayo look.

  • Next Steps:

  • we can try adding more training data for weak classes.

  • Use of data augmentation or fine-tuning with class weighting to improve class balance.

  • Use transfer leeraning from model like Efficientnet,mobilenet